Maosong Sun


2021

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Proceedings of the 20th Chinese National Conference on Computational Linguistics
Sheng Li (李生) | Maosong Sun (孙茂松) | Yang Liu (刘洋) | Hua Wu (吴华) | Kang Liu (刘康) | Wanxiang Che (车万翔) | Shizhu He (何世柱) | Gaoqi Rao (饶高琦)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

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基于BPE分词的中国古诗主题模型及主题可控的诗歌生成(Topic model and topic-controlled poetry generation of Chinese ancient poem based on BPE)
Jiarui Zhang (张家瑞) | Wenhao Li (李文浩) | Maosong Sun (孙茂松)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

“中国古代诗歌是人类文化的瑰宝,其短小精悍的语言却能表达出极其丰富的含义和主题,从古至今吸引了无数的爱好者的欣赏。本文以超过锸锰万首古诗为研究对象,基于BPE算法,按照共现频率对古诗集进行分词,以便于下游任务对古诗的语义进行更准确的理解,我们还将分词后的古诗语料利用隐狄利克雷分配(LDA)模型进行了主题分析。通过比较、调整主题的数量得到了准确度较高的主题模型。更进一步,我们还对语料中的绝句和律诗逐句套用了主题模型,得到了一首诗内部的主题转移矩阵,并进行了一些相关的分析。最后,我们利用了简单的控制码方法将主题模型嵌入到诗歌生成模型中,实现了主题可控的诗歌生成,同时检验了我们训练的主题模型的有效性。”

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Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Zhenghao Liu | Xiaoyuan Yi | Maosong Sun | Liner Yang | Tat-Seng Chua
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious corrections or fail to detect lots of errors. The quality estimation model is necessary to ensure learners get accurate GEC results and avoid misleading from poorly corrected sentences. Well-trained GEC models can generate several high-quality hypotheses through decoding, such as beam search, which provide valuable GEC evidence and can be used to evaluate GEC quality. However, existing models neglect the possible GEC evidence from different hypotheses. This paper presents the Neural Verification Network (VERNet) for GEC quality estimation with multiple hypotheses. VERNet establishes interactions among hypotheses with a reasoning graph and conducts two kinds of attention mechanisms to propagate GEC evidence to verify the quality of generated hypotheses. Our experiments on four GEC datasets show that VERNet achieves state-of-the-art grammatical error detection performance, achieves the best quality estimation results, and significantly improves GEC performance by reranking hypotheses. All data and source codes are available at https://github.com/thunlp/VERNet.

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Open Hierarchical Relation Extraction
Kai Zhang | Yuan Yao | Ruobing Xie | Xu Han | Zhiyuan Liu | Fen Lin | Leyu Lin | Maosong Sun
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Open relation extraction (OpenRE) aims to extract novel relation types from open-domain corpora, which plays an important role in completing the relation schemes of knowledge bases (KBs). Most OpenRE methods cast different relation types in isolation without considering their hierarchical dependency. We argue that OpenRE is inherently in close connection with relation hierarchies. To establish the bidirectional connections between OpenRE and relation hierarchy, we propose the task of open hierarchical relation extraction and present a novel OHRE framework for the task. We propose a dynamic hierarchical triplet objective and hierarchical curriculum training paradigm, to effectively integrate hierarchy information into relation representations for better novel relation extraction. We also present a top-down hierarchy expansion algorithm to add the extracted relations into existing hierarchies with reasonable interpretability. Comprehensive experiments show that OHRE outperforms state-of-the-art models by a large margin on both relation clustering and hierarchy expansion.

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TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference
Deming Ye | Yankai Lin | Yufei Huang | Maosong Sun
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Existing pre-trained language models (PLMs) are often computationally expensive in inference, making them impractical in various resource-limited real-world applications. To address this issue, we propose a dynamic token reduction approach to accelerate PLMs’ inference, named TR-BERT, which could flexibly adapt the layer number of each token in inference to avoid redundant calculation. Specially, TR-BERT formulates the token reduction process as a multi-step token selection problem and automatically learns the selection strategy via reinforcement learning. The experimental results on several downstream NLP tasks show that TR-BERT is able to speed up BERT by 2-5 times to satisfy various performance demands. Moreover, TR-BERT can also achieve better performance with less computation in a suite of long-text tasks since its token-level layer number adaption greatly accelerates the self-attention operation in PLMs. The source code and experiment details of this paper can be obtained from https://github.com/thunlp/TR-BERT.

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Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger
Fanchao Qi | Mukai Li | Yangyi Chen | Zhengyan Zhang | Zhiyuan Liu | Yasheng Wang | Maosong Sun
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Backdoor attacks are a kind of insidious security threat against machine learning models. After being injected with a backdoor in training, the victim model will produce adversary-specified outputs on the inputs embedded with predesigned triggers but behave properly on normal inputs during inference. As a sort of emergent attack, backdoor attacks in natural language processing (NLP) are investigated insufficiently. As far as we know, almost all existing textual backdoor attack methods insert additional contents into normal samples as triggers, which causes the trigger-embedded samples to be detected and the backdoor attacks to be blocked without much effort. In this paper, we propose to use the syntactic structure as the trigger in textual backdoor attacks. We conduct extensive experiments to demonstrate that the syntactic trigger-based attack method can achieve comparable attack performance (almost 100% success rate) to the insertion-based methods but possesses much higher invisibility and stronger resistance to defenses. These results also reveal the significant insidiousness and harmfulness of textual backdoor attacks. All the code and data of this paper can be obtained at https://github.com/thunlp/HiddenKiller.

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ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning
Yujia Qin | Yankai Lin | Ryuichi Takanobu | Zhiyuan Liu | Peng Li | Heng Ji | Minlie Huang | Maosong Sun | Jie Zhou
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks. However, conventional pre-training objectives do not explicitly model relational facts in text, which are crucial for textual understanding. To address this issue, we propose a novel contrastive learning framework ERICA to obtain a deep understanding of the entities and their relations in text. Specifically, we define two novel pre-training tasks to better understand entities and relations: (1) the entity discrimination task to distinguish which tail entity can be inferred by the given head entity and relation; (2) the relation discrimination task to distinguish whether two relations are close or not semantically, which involves complex relational reasoning. Experimental results demonstrate that ERICA can improve typical PLMs (BERT and RoBERTa) on several language understanding tasks, including relation extraction, entity typing and question answering, especially under low-resource settings.

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Mask-Align: Self-Supervised Neural Word Alignment
Chi Chen | Maosong Sun | Yang Liu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods focus on inducing alignments from neural machine translation models, which does not leverage the full context in the target sequence. In this paper, we propose Mask-Align, a self-supervised word alignment model that takes advantage of the full context on the target side. Our model masks out each target token and predicts it conditioned on both source and the remaining target tokens. This two-step process is based on the assumption that the source token contributing most to recovering the masked target token should be aligned. We also introduce an attention variant called leaky attention, which alleviates the problem of unexpected high cross-attention weights on special tokens such as periods. Experiments on four language pairs show that our model outperforms previous unsupervised neural aligners and obtains new state-of-the-art results.

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Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution
Fanchao Qi | Yuan Yao | Sophia Xu | Zhiyuan Liu | Maosong Sun
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks. Injected with backdoors, models perform normally on benign examples but produce attacker-specified predictions when the backdoor is activated, presenting serious security threats to real-world applications. Since existing textual backdoor attacks pay little attention to the invisibility of backdoors, they can be easily detected and blocked. In this work, we present invisible backdoors that are activated by a learnable combination of word substitution. We show that NLP models can be injected with backdoors that lead to a nearly 100% attack success rate, whereas being highly invisible to existing defense strategies and even human inspections. The results raise a serious alarm to the security of NLP models, which requires further research to be resolved. All the data and code of this paper are released at https://github.com/thunlp/BkdAtk-LWS.

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Transfer Learning for Sequence Generation: from Single-source to Multi-source
Xuancheng Huang | Jingfang Xu | Maosong Sun | Yang Liu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Multi-source sequence generation (MSG) is an important kind of sequence generation tasks that takes multiple sources, including automatic post-editing, multi-source translation, multi-document summarization, etc. As MSG tasks suffer from the data scarcity problem and recent pretrained models have been proven to be effective for low-resource downstream tasks, transferring pretrained sequence-to-sequence models to MSG tasks is essential. Although directly finetuning pretrained models on MSG tasks and concatenating multiple sources into a single long sequence is regarded as a simple method to transfer pretrained models to MSG tasks, we conjecture that the direct finetuning method leads to catastrophic forgetting and solely relying on pretrained self-attention layers to capture cross-source information is not sufficient. Therefore, we propose a two-stage finetuning method to alleviate the pretrain-finetune discrepancy and introduce a novel MSG model with a fine encoder to learn better representations in MSG tasks. Experiments show that our approach achieves new state-of-the-art results on the WMT17 APE task and multi-source translation task using the WMT14 test set. When adapted to document-level translation, our framework outperforms strong baselines significantly.

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OpenAttack: An Open-source Textual Adversarial Attack Toolkit
Guoyang Zeng | Fanchao Qi | Qianrui Zhou | Tingji Zhang | Zixian Ma | Bairu Hou | Yuan Zang | Zhiyuan Liu | Maosong Sun
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

Textual adversarial attacking has received wide and increasing attention in recent years. Various attack models have been proposed, which are enormously distinct and implemented with different programming frameworks and settings. These facts hinder quick utilization and fair comparison of attack models. In this paper, we present an open-source textual adversarial attack toolkit named OpenAttack to solve these issues. Compared with existing other textual adversarial attack toolkits, OpenAttack has its unique strengths in support for all attack types, multilinguality, and parallel processing. Currently, OpenAttack includes 15 typical attack models that cover all attack types. Its highly inclusive modular design not only supports quick utilization of existing attack models, but also enables great flexibility and extensibility. OpenAttack has broad uses including comparing and evaluating attack models, measuring robustness of a model, assisting in developing new attack models, and adversarial training. Source code and documentation can be obtained at https://github.com/thunlp/OpenAttack.

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Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction
Tianyu Gao | Xu Han | Yuzhuo Bai | Keyue Qiu | Zhiyu Xie | Yankai Lin | Zhiyuan Liu | Peng Li | Maosong Sun | Jie Zhou
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning
Chenglei Si | Zhengyan Zhang | Fanchao Qi | Zhiyuan Liu | Yasheng Wang | Qun Liu | Maosong Sun
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion
Jie Zhou | Shengding Hu | Xin Lv | Cheng Yang | Zhiyuan Liu | Wei Xu | Jie Jiang | Juanzi Li | Maosong Sun
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Alternated Training with Synthetic and Authentic Data for Neural Machine Translation
Rui Jiao | Zonghan Yang | Maosong Sun | Yang Liu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Automatic Construction of Sememe Knowledge Bases via Dictionaries
Fanchao Qi | Yangyi Chen | Fengyu Wang | Zhiyuan Liu | Xiao Chen | Maosong Sun
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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On the Language Coverage Bias for Neural Machine Translation
Shuo Wang | Zhaopeng Tu | Zhixing Tan | Shuming Shi | Maosong Sun | Yang Liu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Meta-Information Guided Meta-Learning for Few-Shot Relation Classification
Bowen Dong | Yuan Yao | Ruobing Xie | Tianyu Gao | Xu Han | Zhiyuan Liu | Fen Lin | Leyu Lin | Maosong Sun
Proceedings of the 28th International Conference on Computational Linguistics

Few-shot classification requires classifiers to adapt to new classes with only a few training instances. State-of-the-art meta-learning approaches such as MAML learn how to initialize and fast adapt parameters from limited instances, which have shown promising results in few-shot classification. However, existing meta-learning models solely rely on implicit instance-based statistics, and thus suffer from instance unreliability and weak interpretability. To solve this problem, we propose a novel meta-information guided meta-learning (MIML) framework, where semantic concepts of classes provide strong guidance for meta-learning in both initialization and adaptation. In effect, our model can establish connections between instance-based information and semantic-based information, which enables more effective initialization and faster adaptation. Comprehensive experimental results on few-shot relation classification demonstrate the effectiveness of the proposed framework. Notably, MIML achieves comparable or superior performance to humans with only one shot on FewRel evaluation.

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Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet
Bairu Hou | Fanchao Qi | Yuan Zang | Xurui Zhang | Zhiyuan Liu | Maosong Sun
Proceedings of the 28th International Conference on Computational Linguistics

Word sense disambiguation (WSD) is a fundamental natural language processing task. Unsupervised knowledge-based WSD only relies on a lexical knowledge base as the sense inventory and has wider practical use than supervised WSD that requires a mass of sense-annotated data. HowNet is the most widely used lexical knowledge base in Chinese WSD. Because of its uniqueness, however, most of existing unsupervised WSD methods cannot work for HowNet-based WSD, and the tailor-made methods have not obtained satisfying results. In this paper, we propose a new unsupervised method for HowNet-based Chinese WSD, which exploits the masked language model task of pre-trained language models. In experiments, considering existing evaluation dataset is small and out-of-date, we build a new and larger HowNet-based WSD dataset. Experimental results demonstrate that our model achieves significantly better performance than all the baseline methods. All the code and data of this paper are available at https://github.com/thunlp/SememeWSD.

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Generating Major Types of Chinese Classical Poetry in a Uniformed Framework
Jinyi Hu | Maosong Sun
Proceedings of the 12th Language Resources and Evaluation Conference

Poetry generation is an interesting research topic in the field of text generation. As one of the most valuable literary and cultural heritages of China, Chinese classical poetry is very familiar and loved by Chinese people from generation to generation. It has many particular characteristics in its language structure, ranging from form, sound to meaning, thus is regarded as an ideal testing task for text generation. In this paper, we propose a GPT-2 based uniformed framework for generating major types of Chinese classical poems. We define a unified format for formulating all types of training samples by integrating detailed form information, then present a simple form- stressed weighting method in GPT-2 to strengthen the control to the form of the generated poems, with special emphasis on those forms with longer body length. Preliminary experimental results show this enhanced model can generate Chinese classical poems of major types with high quality in both form and content, validating the effectiveness of the proposed strategy. The model has been incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University.

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Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling
Zhenghao Liu | Chenyan Xiong | Zhuyun Dai | Si Sun | Maosong Sun | Zhiyuan Liu
Findings of the Association for Computational Linguistics: EMNLP 2020

With the epidemic of COVID-19, verifying the scientifically false online information, such as fake news and maliciously fabricated statements, has become crucial. However, the lack of training data in the scientific domain limits the performance of fact verification models. This paper proposes an in-domain language modeling method for fact extraction and verification systems. We come up with SciKGAT to combine the advantages of open-domain literature search, state-of-the-art fact verification systems and in-domain medical knowledge through language modeling. Our experiments on SCIFACT, a dataset of expert-written scientific fact verification, show that SciKGAT achieves 30% absolute improvement on precision. Our analyses show that such improvement thrives from our in-domain language model by picking up more related evidence pieces and accurate fact verification. Our codes and data are released via Github.

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How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence
Haoxi Zhong | Chaojun Xiao | Cunchao Tu | Tianyang Zhang | Zhiyuan Liu | Maosong Sun
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. In recent years, LegalAI has drawn increasing attention rapidly from both AI researchers and legal professionals, as LegalAI is beneficial to the legal system for liberating legal professionals from a maze of paperwork. Legal professionals often think about how to solve tasks from rule-based and symbol-based methods, while NLP researchers concentrate more on data-driven and embedding methods. In this paper, we introduce the history, the current state, and the future directions of research in LegalAI. We illustrate the tasks from the perspectives of legal professionals and NLP researchers and show several representative applications in LegalAI. We conduct experiments and provide an in-depth analysis of the advantages and disadvantages of existing works to explore possible future directions. You can find the implementation of our work from https://github.com/thunlp/CLAIM.

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Word-level Textual Adversarial Attacking as Combinatorial Optimization
Yuan Zang | Fanchao Qi | Chenghao Yang | Zhiyuan Liu | Meng Zhang | Qun Liu | Maosong Sun
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Adversarial attacks are carried out to reveal the vulnerability of deep neural networks. Textual adversarial attacking is challenging because text is discrete and a small perturbation can bring significant change to the original input. Word-level attacking, which can be regarded as a combinatorial optimization problem, is a well-studied class of textual attack methods. However, existing word-level attack models are far from perfect, largely because unsuitable search space reduction methods and inefficient optimization algorithms are employed. In this paper, we propose a novel attack model, which incorporates the sememe-based word substitution method and particle swarm optimization-based search algorithm to solve the two problems separately. We conduct exhaustive experiments to evaluate our attack model by attacking BiLSTM and BERT on three benchmark datasets. Experimental results demonstrate that our model consistently achieves much higher attack success rates and crafts more high-quality adversarial examples as compared to baseline methods. Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training. All the code and data of this paper can be obtained on https://github.com/thunlp/SememePSO-Attack.

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Continual Relation Learning via Episodic Memory Activation and Reconsolidation
Xu Han | Yi Dai | Tianyu Gao | Yankai Lin | Zhiyuan Liu | Peng Li | Maosong Sun | Jie Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations. Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent training is an effective solution for such a challenging problem. However, these memory-based methods usually suffer from overfitting the few memorized examples of old relations, which may gradually cause inevitable confusion among existing relations. Inspired by the mechanism in human long-term memory formation, we introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning. Every time neural models are activated to learn both new and memorized data, EMAR utilizes relation prototypes for memory reconsolidation exercise to keep a stable understanding of old relations. The experimental results show that EMAR could get rid of catastrophically forgetting old relations and outperform the state-of-the-art continual learning models.

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Fine-grained Fact Verification with Kernel Graph Attention Network
Zhenghao Liu | Chenyan Xiong | Maosong Sun | Zhiyuan Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Fact Verification requires fine-grained natural language inference capability that finds subtle clues to identify the syntactical and semantically correct but not well-supported claims. This paper presents Kernel Graph Attention Network (KGAT), which conducts more fine-grained fact verification with kernel-based attentions. Given a claim and a set of potential evidence sentences that form an evidence graph, KGAT introduces node kernels, which better measure the importance of the evidence node, and edge kernels, which conduct fine-grained evidence propagation in the graph, into Graph Attention Networks for more accurate fact verification. KGAT achieves a 70.38% FEVER score and significantly outperforms existing fact verification models on FEVER, a large-scale benchmark for fact verification. Our analyses illustrate that, compared to dot-product attentions, the kernel-based attention concentrates more on relevant evidence sentences and meaningful clues in the evidence graph, which is the main source of KGAT’s effectiveness. All source codes of this work are available at https://github.com/thunlp/KernelGAT.

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Learning from Context or Names? An Empirical Study on Neural Relation Extraction
Hao Peng | Tianyu Gao | Xu Han | Yankai Lin | Peng Li | Zhiyuan Liu | Maosong Sun | Jie Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Neural models have achieved remarkable success on relation extraction (RE) benchmarks. However, there is no clear understanding what information in text affects existing RE models to make decisions and how to further improve the performance of these models. To this end, we empirically study the effect of two main information sources in text: textual context and entity mentions (names). We find that (i) while context is the main source to support the predictions, RE models also heavily rely on the information from entity mentions, most of which is type information, and (ii) existing datasets may leak shallow heuristics via entity mentions and thus contribute to the high performance on RE benchmarks. Based on the analyses, we propose an entity-masked contrastive pre-training framework for RE to gain a deeper understanding on both textual context and type information while avoiding rote memorization of entities or use of superficial cues in mentions. We carry out extensive experiments to support our views, and show that our framework can improve the effectiveness and robustness of neural models in different RE scenarios. All the code and datasets are released at https://github.com/thunlp/RE-Context-or-Names.

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Denoising Relation Extraction from Document-level Distant Supervision
Chaojun Xiao | Yuan Yao | Ruobing Xie | Xu Han | Zhiyuan Liu | Maosong Sun | Fen Lin | Leyu Lin
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Distant supervision (DS) has been widely adopted to generate auto-labeled data for sentence-level relation extraction (RE) and achieved great results. However, the existing success of DS cannot be directly transferred to more challenging document-level relation extraction (DocRE), as the inevitable noise caused by DS may be even multiplied in documents and significantly harm the performance of RE. To alleviate this issue, we propose a novel pre-trained model for DocRE, which de-emphasize noisy DS data via multiple pre-training tasks. The experimental results on the large-scale DocRE benchmark show that our model can capture useful information from noisy data and achieve promising results.

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Train No Evil: Selective Masking for Task-Guided Pre-Training
Yuxian Gu | Zhengyan Zhang | Xiaozhi Wang | Zhiyuan Liu | Maosong Sun
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Recently, pre-trained language models mostly follow the pre-train-then-fine-tuning paradigm and have achieved great performance on various downstream tasks. However, since the pre-training stage is typically task-agnostic and the fine-tuning stage usually suffers from insufficient supervised data, the models cannot always well capture the domain-specific and task-specific patterns. In this paper, we propose a three-stage framework by adding a task-guided pre-training stage with selective masking between general pre-training and fine-tuning. In this stage, the model is trained by masked language modeling on in-domain unsupervised data to learn domain-specific patterns and we propose a novel selective masking strategy to learn task-specific patterns. Specifically, we design a method to measure the importance of each token in sequences and selectively mask the important tokens. Experimental results on two sentiment analysis tasks show that our method can achieve comparable or even better performance with less than 50% of computation cost, which indicates our method is both effective and efficient. The source code of this paper can be obtained from https://github.com/thunlp/SelectiveMasking.

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Coreferential Reasoning Learning for Language Representation
Deming Ye | Yankai Lin | Jiaju Du | Zhenghao Liu | Peng Li | Maosong Sun | Zhiyuan Liu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Language representation models such as BERT could effectively capture contextual semantic information from plain text, and have been proved to achieve promising results in lots of downstream NLP tasks with appropriate fine-tuning. However, most existing language representation models cannot explicitly handle coreference, which is essential to the coherent understanding of the whole discourse. To address this issue, we present CorefBERT, a novel language representation model that can capture the coreferential relations in context. The experimental results show that, compared with existing baseline models, CorefBERT can achieve significant improvements consistently on various downstream NLP tasks that require coreferential reasoning, while maintaining comparable performance to previous models on other common NLP tasks. The source code and experiment details of this paper can be obtained from https://github.com/thunlp/CorefBERT.

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WantWords: An Open-source Online Reverse Dictionary System
Fanchao Qi | Lei Zhang | Yanhui Yang | Zhiyuan Liu | Maosong Sun
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

A reverse dictionary takes descriptions of words as input and outputs words semantically matching the input descriptions. Reverse dictionaries have great practical value such as solving the tip-of-the-tongue problem and helping new language learners. There have been some online reverse dictionary systems, but they support English reverse dictionary queries only and their performance is far from perfect. In this paper, we present a new open-source online reverse dictionary system named WantWords (https://wantwords.thunlp.org/). It not only significantly outperforms other reverse dictionary systems on English reverse dictionary performance, but also supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries for the first time. Moreover, it has user-friendly front-end design which can help users find the words they need quickly and easily. All the code and data are available at https://github.com/thunlp/WantWords.

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IsOBS: An Information System for Oracle Bone Script
Xu Han | Yuzhuo Bai | Keyue Qiu | Zhiyuan Liu | Maosong Sun
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Oracle bone script (OBS) is the earliest known ancient Chinese writing system and the ancestor of modern Chinese. As the Chinese writing system is the oldest continuously-used system in the world, the study of OBS plays an important role in both linguistic and historical research. In order to utilize advanced machine learning methods to automatically process OBS, we construct an information system for OBS (IsOBS) to symbolize, serialize, and store OBS data at the character-level, based on efficient databases and retrieval modules. Moreover, we also apply few-shot learning methods to build an effective OBS character recognition module, which can recognize a large number of OBS characters (especially those characters with a handful of examples) and make the system easy to use. The demo system of IsOBS can be found from http://isobs.thunlp.org/. In the future, we will add more OBS data to the system, and hopefully our IsOBS can support further efforts in automatically processing OBS and advance the scientific progress in this field.

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More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
Xu Han | Tianyu Gao | Yankai Lin | Hao Peng | Yaoliang Yang | Chaojun Xiao | Zhiyuan Liu | Peng Li | Jie Zhou | Maosong Sun
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require “more” from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.

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Proceedings of the 19th Chinese National Conference on Computational Linguistics
Maosong Sun (孙茂松) | Sujian Li (李素建) | Yue Zhang (张岳) | Yang Liu (刘洋)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

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THUMT: An Open-Source Toolkit for Neural Machine Translation
Zhixing Tan | Jiacheng Zhang | Xuancheng Huang | Gang Chen | Shuo Wang | Maosong Sun | Huanbo Luan | Yang Liu
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

2019

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Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data
Ruidong Wu | Yuan Yao | Xu Han | Ruobing Xie | Zhiyuan Liu | Fen Lin | Leyu Lin | Maosong Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Open relation extraction (OpenRE) aims to extract relational facts from the open-domain corpus. To this end, it discovers relation patterns between named entities and then clusters those semantically equivalent patterns into a united relation cluster. Most OpenRE methods typically confine themselves to unsupervised paradigms, without taking advantage of existing relational facts in knowledge bases (KBs) and their high-quality labeled instances. To address this issue, we propose Relational Siamese Networks (RSNs) to learn similarity metrics of relations from labeled data of pre-defined relations, and then transfer the relational knowledge to identify novel relations in unlabeled data. Experiment results on two real-world datasets show that our framework can achieve significant improvements as compared with other state-of-the-art methods. Our code is available at https://github.com/thunlp/RSN.

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Improving Back-Translation with Uncertainty-based Confidence Estimation
Shuo Wang | Yang Liu | Chao Wang | Huanbo Luan | Maosong Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy. In this work, we propose to quantify the confidence of NMT model predictions based on model uncertainty. With word- and sentence-level confidence measures based on uncertainty, it is possible for back-translation to better cope with noise in synthetic bilingual corpora. Experiments on Chinese-English and English-German translation tasks show that uncertainty-based confidence estimation significantly improves the performance of back-translation.

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HMEAE: Hierarchical Modular Event Argument Extraction
Xiaozhi Wang | Ziqi Wang | Xu Han | Zhiyuan Liu | Juanzi Li | Peng Li | Maosong Sun | Jie Zhou | Xiang Ren
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Existing event extraction methods classify each argument role independently, ignoring the conceptual correlations between different argument roles. In this paper, we propose a Hierarchical Modular Event Argument Extraction (HMEAE) model, to provide effective inductive bias from the concept hierarchy of event argument roles. Specifically, we design a neural module network for each basic unit of the concept hierarchy, and then hierarchically compose relevant unit modules with logical operations into a role-oriented modular network to classify a specific argument role. As many argument roles share the same high-level unit module, their correlation can be utilized to extract specific event arguments better. Experiments on real-world datasets show that HMEAE can effectively leverage useful knowledge from the concept hierarchy and significantly outperform the state-of-the-art baselines. The source code can be obtained from https://github.com/thunlp/HMEAE.

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Learning to Copy for Automatic Post-Editing
Xuancheng Huang | Yang Liu | Huanbo Luan | Jingfang Xu | Maosong Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Automatic post-editing (APE), which aims to correct errors in the output of machine translation systems in a post-processing step, is an important task in natural language processing. While recent work has achieved considerable performance gains by using neural networks, how to model the copying mechanism for APE remains a challenge. In this work, we propose a new method for modeling copying for APE. To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way. These representations are used to explicitly indicate which words in the system outputs should be copied. Finally, CopyNet (Gu et.al., 2016) can be combined with our method to place the copied words in correct positions in post-edited translations. Experiments on the datasets of the WMT 2016-2017 APE shared tasks show that our approach outperforms all best published results.

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FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
Tianyu Gao | Xu Han | Hao Zhu | Zhiyuan Liu | Peng Li | Maosong Sun | Jie Zhou
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? (2) Can they detect none-of-the-above (NOTA) relations? To construct FewRel 2.0, we build upon the FewRel dataset by adding a new test set in a quite different domain, and a NOTA relation choice. With the new dataset and extensive experimental analysis, we found (1) that the state-of-the-art few-shot relation classification models struggle on these two aspects, and (2) that the commonly-used techniques for domain adaptation and NOTA detection still cannot handle the two challenges well. Our research calls for more attention and further efforts to these two real-world issues. All details and resources about the dataset and baselines are released at https://github.com/thunlp/fewrel.

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OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction
Xu Han | Tianyu Gao | Yuan Yao | Deming Ye | Zhiyuan Liu | Maosong Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE). Specifically, by implementing typical RE methods, OpenNRE not only allows developers to train custom models to extract structured relational facts from the plain text but also supports quick model validation for researchers. Besides, OpenNRE provides various functional RE modules based on both TensorFlow and PyTorch to maintain sufficient modularity and extensibility, making it becomes easy to incorporate new models into the framework. Besides the toolkit, we also release an online system to meet real-time extraction without any training and deploying. Meanwhile, the online system can extract facts in various scenarios as well as aligning the extracted facts to Wikidata, which may benefit various downstream knowledge-driven applications (e.g., information retrieval and question answering). More details of the toolkit and online system can be obtained from http://github.com/thunlp/OpenNRE.

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Adversarial Training for Weakly Supervised Event Detection
Xiaozhi Wang | Xu Han | Zhiyuan Liu | Maosong Sun | Peng Li
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Modern weakly supervised methods for event detection (ED) avoid time-consuming human annotation and achieve promising results by learning from auto-labeled data. However, these methods typically rely on sophisticated pre-defined rules as well as existing instances in knowledge bases for automatic annotation and thus suffer from low coverage, topic bias, and data noise. To address these issues, we build a large event-related candidate set with good coverage and then apply an adversarial training mechanism to iteratively identify those informative instances from the candidate set and filter out those noisy ones. The experiments on two real-world datasets show that our candidate selection and adversarial training can cooperate together to obtain more diverse and accurate training data for ED, and significantly outperform the state-of-the-art methods in various weakly supervised scenarios. The datasets and source code can be obtained from https://github.com/thunlp/Adv-ED.

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DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Yuan Yao | Deming Ye | Peng Li | Xu Han | Yankai Lin | Zhenghao Liu | Zhiyuan Liu | Lixin Huang | Jie Zhou | Maosong Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: (1) DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text; (2) DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document; (3) along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios. In order to verify the challenges of document-level RE, we implement recent state-of-the-art methods for RE and conduct a thorough evaluation of these methods on DocRED. Empirical results show that DocRED is challenging for existing RE methods, which indicates that document-level RE remains an open problem and requires further efforts. Based on the detailed analysis on the experiments, we discuss multiple promising directions for future research. We make DocRED and the code for our baselines publicly available at https://github.com/thunlp/DocRED.

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GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification
Jie Zhou | Xu Han | Cheng Yang | Zhiyuan Liu | Lifeng Wang | Changcheng Li | Maosong Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract information from evidence without letting evidence communicate with each other, e.g., merely concatenate the evidence for processing. Therefore, these methods are unable to grasp sufficient relational and logical information among the evidence. To alleviate this issue, we propose a graph-based evidence aggregating and reasoning (GEAR) framework which enables information to transfer on a fully-connected evidence graph and then utilizes different aggregators to collect multi-evidence information. We further employ BERT, an effective pre-trained language representation model, to improve the performance. Experimental results on a large-scale benchmark dataset FEVER have demonstrated that GEAR could leverage multi-evidence information for FV and thus achieves the promising result with a test FEVER score of 67.10%. Our code is available at https://github.com/thunlp/GEAR.

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Graph Neural Networks with Generated Parameters for Relation Extraction
Hao Zhu | Yankai Lin | Zhiyuan Liu | Jie Fu | Tat-Seng Chua | Maosong Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we propose a novel graph neural network with generated parameters (GP-GNNs). The parameters in the propagation module, i.e. the transition matrices used in message passing procedure, are produced by a generator taking natural language sentences as inputs. We verify GP-GNNs in relation extraction from text, both on bag- and instance-settings. Experimental results on a human-annotated dataset and two distantly supervised datasets show that multi-hop reasoning mechanism yields significant improvements. We also perform a qualitative analysis to demonstrate that our model could discover more accurate relations by multi-hop relational reasoning.

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ERNIE: Enhanced Language Representation with Informative Entities
Zhengyan Zhang | Xu Han | Zhiyuan Liu | Xin Jiang | Maosong Sun | Qun Liu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks. However, the existing pre-trained language models rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better language understanding. We argue that informative entities in KGs can enhance language representation with external knowledge. In this paper, we utilize both large-scale textual corpora and KGs to train an enhanced language representation model (ERNIE), which can take full advantage of lexical, syntactic, and knowledge information simultaneously. The experimental results have demonstrated that ERNIE achieves significant improvements on various knowledge-driven tasks, and meanwhile is comparable with the state-of-the-art model BERT on other common NLP tasks. The code and datasets will be available in the future.

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XQA: A Cross-lingual Open-domain Question Answering Dataset
Jiahua Liu | Yankai Lin | Zhiyuan Liu | Maosong Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Open-domain question answering (OpenQA) aims to answer questions through text retrieval and reading comprehension. Recently, lots of neural network-based models have been proposed and achieved promising results in OpenQA. However, the success of these models relies on a massive volume of training data (usually in English), which is not available in many other languages, especially for those low-resource languages. Therefore, it is essential to investigate cross-lingual OpenQA. In this paper, we construct a novel dataset XQA for cross-lingual OpenQA research. It consists of a training set in English as well as development and test sets in eight other languages. Besides, we provide several baseline systems for cross-lingual OpenQA, including two machine translation-based methods and one zero-shot cross-lingual method (multilingual BERT). Experimental results show that the multilingual BERT model achieves the best results in almost all target languages, while the performance of cross-lingual OpenQA is still much lower than that of English. Our analysis indicates that the performance of cross-lingual OpenQA is related to not only how similar the target language and English are, but also how difficult the question set of the target language is. The XQA dataset is publicly available at http://github.com/thunlp/XQA.

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Quantifying Similarity between Relations with Fact Distribution
Weize Chen | Hao Zhu | Xu Han | Zhiyuan Liu | Maosong Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases. Specifically, our approach is based on the divergence between the conditional probability distributions over entity pairs. In this paper, these distributions are parameterized by a very simple neural network. Although computing the exact similarity is in-tractable, we provide a sampling-based method to get a good approximation. We empirically show the outputs of our approach significantly correlate with human judgments. By applying our method to various tasks, we also find that (1) our approach could effectively detect redundant relations extracted by open information extraction (Open IE) models, that (2) even the most competitive models for relational classification still make mistakes among very similar relations, and that (3) our approach could be incorporated into negative sampling and softmax classification to alleviate these mistakes.

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Modeling Semantic Compositionality with Sememe Knowledge
Fanchao Qi | Junjie Huang | Chenghao Yang | Zhiyuan Liu | Xiao Chen | Qun Liu | Maosong Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models. In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment. Furthermore, we make the first attempt to incorporate sememe knowledge into SC models, and employ the sememe-incorporated models in learning representations of multiword expressions, a typical task of SC. In experiments, we implement our models by incorporating knowledge from a famous sememe knowledge base HowNet and perform both intrinsic and extrinsic evaluations. Experimental results show that our models achieve significant performance boost as compared to the baseline methods without considering sememe knowledge. We further conduct quantitative analysis and case studies to demonstrate the effectiveness of applying sememe knowledge in modeling SC.All the code and data of this paper can be obtained on https://github.com/thunlp/Sememe-SC.

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Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach
Zonghan Yang | Yong Cheng | Yang Liu | Maosong Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

While neural machine translation (NMT) has achieved remarkable success, NMT systems are prone to make word omission errors. In this work, we propose a contrastive learning approach to reducing word omission errors in NMT. The basic idea is to enable the NMT model to assign a higher probability to a ground-truth translation and a lower probability to an erroneous translation, which is automatically constructed from the ground-truth translation by omitting words. We design different types of negative examples depending on the number of omitted words, word frequency, and part of speech. Experiments on Chinese-to-English, German-to-English, and Russian-to-English translation tasks show that our approach is effective in reducing word omission errors and achieves better translation performance than three baseline methods.

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Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System
Guo Zhipeng | Xiaoyuan Yi | Maosong Sun | Wenhao Li | Cheng Yang | Jiannan Liang | Huimin Chen | Yuhui Zhang | Ruoyu Li
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Research on the automatic generation of poetry, the treasure of human culture, has lasted for decades. Most existing systems, however, are merely model-oriented, which input some user-specified keywords and directly complete the generation process in one pass, with little user participation. We believe that the machine, being a collaborator or an assistant, should not replace human beings in poetic creation. Therefore, we proposed Jiuge, a human-machine collaborative Chinese classical poetry generation system. Unlike previous systems, Jiuge allows users to revise the unsatisfied parts of a generated poem draft repeatedly. According to the revision, the poem will be dynamically updated and regenerated. After the revision and modification procedure, the user can write a satisfying poem together with Jiuge system collaboratively. Besides, Jiuge can accept multi-modal inputs, such as keywords, plain text or images. By exposing the options of poetry genres, styles and revision modes, Jiuge, acting as a professional assistant, allows constant and active participation of users in poetic creation.

2018

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Few-Shot Charge Prediction with Discriminative Legal Attributes
Zikun Hu | Xiang Li | Cunchao Tu | Zhiyuan Liu | Maosong Sun
Proceedings of the 27th International Conference on Computational Linguistics

Automatic charge prediction aims to predict the final charges according to the fact descriptions in criminal cases and plays a crucial role in legal assistant systems. Existing works on charge prediction perform adequately on those high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. Moreover, these exist many confusing charge pairs, whose fact descriptions are fairly similar to each other. To address these issues, we introduce several discriminative attributes of charges as the internal mapping between fact descriptions and charges. These attributes provide additional information for few-shot charges, as well as effective signals for distinguishing confusing charges. More specifically, we propose an attribute-attentive charge prediction model to infer the attributes and charges simultaneously. Experimental results on real-work datasets demonstrate that our proposed model achieves significant and consistent improvements than other state-of-the-art baselines. Specifically, our model outperforms other baselines by more than 50% in the few-shot scenario. Our codes and datasets can be obtained from https://github.com/thunlp/attribute_charge.

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Adversarial Multi-lingual Neural Relation Extraction
Xiaozhi Wang | Xu Han | Yankai Lin | Zhiyuan Liu | Maosong Sun
Proceedings of the 27th International Conference on Computational Linguistics

Multi-lingual relation extraction aims to find unknown relational facts from text in various languages. Existing models cannot well capture the consistency and diversity of relation patterns in different languages. To address these issues, we propose an adversarial multi-lingual neural relation extraction (AMNRE) model, which builds both consistent and individual representations for each sentence to consider the consistency and diversity among languages. Further, we adopt an adversarial training strategy to ensure those consistent sentence representations could effectively extract the language-consistent relation patterns. The experimental results on real-world datasets demonstrate that our AMNRE model significantly outperforms the state-of-the-art models. The source code of this paper can be obtained from https://github.com/thunlp/AMNRE.

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Chinese Poetry Generation with a Salient-Clue Mechanism
Xiaoyuan Yi | Ruoyu Li | Maosong Sun
Proceedings of the 22nd Conference on Computational Natural Language Learning

As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations. Automatic poetry composition is a challenge for AI. In recent years, significant progress has been made in this area benefiting from the development of neural networks. However, the coherence in meaning, theme or even artistic conception for a generated poem as a whole still remains a big problem. In this paper, we propose a novel Salient-Clue mechanism for Chinese poetry generation. Different from previous work which tried to exploit all the context information, our model selects the most salient characters automatically from each so-far generated line to gradually form a salient clue, which is utilized to guide successive poem generation process so as to eliminate interruptions and improve coherence. Besides, our model can be flexibly extended to control the generated poem in different aspects, for example, poetry style, which further enhances the coherence. Experimental results show that our model is very effective, outperforming three strong baselines.

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Denoising Distantly Supervised Open-Domain Question Answering
Yankai Lin | Haozhe Ji | Zhiyuan Liu | Maosong Sun
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Distantly supervised open-domain question answering (DS-QA) aims to find answers in collections of unlabeled text. Existing DS-QA models usually retrieve related paragraphs from a large-scale corpus and apply reading comprehension technique to extract answers from the most relevant paragraph. They ignore the rich information contained in other paragraphs. Moreover, distant supervision data inevitably accompanies with the wrong labeling problem, and these noisy data will substantially degrade the performance of DS-QA. To address these issues, we propose a novel DS-QA model which employs a paragraph selector to filter out those noisy paragraphs and a paragraph reader to extract the correct answer from those denoised paragraphs. Experimental results on real-world datasets show that our model can capture useful information from noisy data and achieve significant improvements on DS-QA as compared to all baselines.

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Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
Zhenghao Liu | Chenyan Xiong | Maosong Sun | Zhiyuan Liu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words and entity annotations. The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks. The two components are learned end-to-end, making EDRM a natural combination of entity-oriented search and neural information retrieval. Our experiments on a commercial search log demonstrate the effectiveness of EDRM. Our analyses reveal that knowledge graph semantics significantly improve the generalization ability of neural ranking models.

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Incorporating Chinese Characters of Words for Lexical Sememe Prediction
Huiming Jin | Hao Zhu | Zhiyuan Liu | Ruobing Xie | Maosong Sun | Fen Lin | Leyu Lin
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their sememes by linguists, and form linguistic common-sense knowledge bases widely used in various NLP tasks. Recently, the lexical sememe prediction task has been introduced. It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency. However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words. To address this issue for Chinese, we propose a novel framework to take advantage of both internal character information and external context information of words. We experiment on HowNet, a Chinese sememe knowledge base, and demonstrate that our framework outperforms state-of-the-art baselines by a large margin, and maintains a robust performance even for low-frequency words.

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Cross-lingual Lexical Sememe Prediction
Fanchao Qi | Yankai Lin | Maosong Sun | Hao Zhu | Ruobing Xie | Zhiyuan Liu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Sememes are defined as the minimum semantic units of human languages. As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks. However, most languages still do not have sememe-based linguistic knowledge bases. Thus we present a task of cross-lingual lexical sememe prediction, aiming to automatically predict sememes for words in other languages. We propose a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction. Experimental results on real-world datasets show that our proposed model achieves consistent and significant improvements as compared to baseline methods in cross-lingual sememe prediction. The codes and data of this paper are available at https://github.com/thunlp/CL-SP.

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Improving the Transformer Translation Model with Document-Level Context
Jiacheng Zhang | Huanbo Luan | Maosong Sun | Feifei Zhai | Jingfang Xu | Min Zhang | Yang Liu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge. In this work, we extend the Transformer model with a new context encoder to represent document-level context, which is then incorporated into the original encoder and decoder. As large-scale document-level parallel corpora are usually not available, we introduce a two-step training method to take full advantage of abundant sentence-level parallel corpora and limited document-level parallel corpora. Experiments on the NIST Chinese-English datasets and the IWSLT French-English datasets show that our approach improves over Transformer significantly.

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Put It Back: Entity Typing with Language Model Enhancement
Ji Xin | Hao Zhu | Xu Han | Zhiyuan Liu | Maosong Sun
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Entity typing aims to classify semantic types of an entity mention in a specific context. Most existing models obtain training data using distant supervision, and inevitably suffer from the problem of noisy labels. To address this issue, we propose entity typing with language model enhancement. It utilizes a language model to measure the compatibility between context sentences and labels, and thereby automatically focuses more on context-dependent labels. Experiments on benchmark datasets demonstrate that our method is capable of enhancing the entity typing model with information from the language model, and significantly outperforms the state-of-the-art baseline. Code and data for this paper can be found from https://github.com/thunlp/LME.

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A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension
Jiahua Liu | Wan Wei | Maosong Sun | Hao Chen | Yantao Du | Dekang Lin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The task of machine reading comprehension (MRC) has evolved from answering simple questions from well-edited text to answering real questions from users out of web data. In the real-world setting, full-body text from multiple relevant documents in the top search results are provided as context for questions from user queries, including not only questions with a single, short, and factual answer, but also questions about reasons, procedures, and opinions. In this case, multiple answers could be equally valid for a single question and each answer may occur multiple times in the context, which should be taken into consideration when we build MRC system. We propose a multi-answer multi-task framework, in which different loss functions are used for multiple reference answers. Minimum Risk Training is applied to solve the multi-occurrence problem of a single answer. Combined with a simple heuristic passage extraction strategy for overlong documents, our model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09.

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Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention
Xu Han | Pengfei Yu | Zhiyuan Liu | Maosong Sun | Peng Li
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Distantly supervised relation extraction employs existing knowledge graphs to automatically collect training data. While distant supervision is effective to scale relation extraction up to large-scale corpora, it inevitably suffers from the wrong labeling problem. Many efforts have been devoted to identifying valid instances from noisy data. However, most existing methods handle each relation in isolation, regardless of rich semantic correlations located in relation hierarchies. In this paper, we aim to incorporate the hierarchical information of relations for distantly supervised relation extraction and propose a novel hierarchical attention scheme. The multiple layers of our hierarchical attention scheme provide coarse-to-fine granularity to better identify valid instances, which is especially effective for extracting those long-tail relations. The experimental results on a large-scale benchmark dataset demonstrate that our models are capable of modeling the hierarchical information of relations and significantly outperform other baselines. The source code of this paper can be obtained from https://github.com/thunlp/HNRE.

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Automatic Poetry Generation with Mutual Reinforcement Learning
Xiaoyuan Yi | Maosong Sun | Ruoyu Li | Wenhao Li
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Poetry is one of the most beautiful forms of human language art. As a crucial step towards computer creativity, automatic poetry generation has drawn researchers’ attention for decades. In recent years, some neural models have made remarkable progress in this task. However, they are all based on maximum likelihood estimation, which only learns common patterns of the corpus and results in loss-evaluation mismatch. Human experts evaluate poetry in terms of some specific criteria, instead of word-level likelihood. To handle this problem, we directly model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning, so as to motivate the model to pursue higher scores. Besides, inspired by writing theories, we propose a novel mutual reinforcement learning schema. We simultaneously train two learners (generators) which learn not only from the teacher (rewarder) but also from each other to further improve performance. We experiment on Chinese poetry. Based on a strong basic model, our method achieves better results and outperforms the current state-of-the-art method.

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Legal Judgment Prediction via Topological Learning
Haoxi Zhong | Zhipeng Guo | Cunchao Tu | Chaojun Xiao | Zhiyuan Liu | Maosong Sun
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Legal Judgment Prediction (LJP) aims to predict the judgment result based on the facts of a case and becomes a promising application of artificial intelligence techniques in the legal field. In real-world scenarios, legal judgment usually consists of multiple subtasks, such as the decisions of applicable law articles, charges, fines, and the term of penalty. Moreover, there exist topological dependencies among these subtasks. While most existing works only focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks, we formalize the dependencies among subtasks as a Directed Acyclic Graph (DAG) and propose a topological multi-task learning framework, TopJudge, which incorporates multiple subtasks and DAG dependencies into judgment prediction. We conduct experiments on several real-world large-scale datasets of criminal cases in the civil law system. Experimental results show that our model achieves consistent and significant improvements over baselines on all judgment prediction tasks. The source code can be obtained from https://github.com/thunlp/TopJudge.

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Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement
Cheng Yang | Maosong Sun | Xiaoyuan Yi | Wenhao Li
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The ability to write diverse poems in different styles under the same poetic imagery is an important characteristic of human poetry writing. Most previous works on automatic Chinese poetry generation focused on improving the coherency among lines. Some work explored style transfer but suffered from expensive expert labeling of poem styles. In this paper, we target on stylistic poetry generation in a fully unsupervised manner for the first time. We propose a novel model which requires no supervised style labeling by incorporating mutual information, a concept in information theory, into modeling. Experimental results show that our model is able to generate stylistic poems without losing fluency and coherency.

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Language Modeling with Sparse Product of Sememe Experts
Yihong Gu | Jun Yan | Hao Zhu | Zhiyuan Liu | Ruobing Xie | Maosong Sun | Fen Lin | Leyu Lin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words. In this paper, we argue that words are atomic language units but not necessarily atomic semantic units. Inspired by HowNet, we use sememes, the minimum semantic units in human languages, to represent the implicit semantics behind words for language modeling, named Sememe-Driven Language Model (SDLM). More specifically, to predict the next word, SDLM first estimates the sememe distribution given textual context. Afterwards, it regards each sememe as a distinct semantic expert, and these experts jointly identify the most probable senses and the corresponding word. In this way, SDLM enables language models to work beyond word-level manipulation to fine-grained sememe-level semantics, and offers us more powerful tools to fine-tune language models and improve the interpretability as well as the robustness of language models. Experiments on language modeling and the downstream application of headline generation demonstrate the significant effectiveness of SDLM.

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FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
Xu Han | Hao Zhu | Pengfei Yu | Ziyun Wang | Yuan Yao | Zhiyuan Liu | Maosong Sun
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present a Few-Shot Relation Classification Dataset (dataset), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research.

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OpenKE: An Open Toolkit for Knowledge Embedding
Xu Han | Shulin Cao | Xin Lv | Yankai Lin | Zhiyuan Liu | Maosong Sun | Juanzi Li
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes operational efficiency to support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity and extensibility to easily incorporate new models into the framework. Besides the toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are also available, which can be directly applied for many applications including information retrieval, personalized recommendation and question answering. The toolkit, documentation, and pre-trained embeddings are all released on http://openke.thunlp.org/.

2017

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Domain-Specific New Words Detection in Chinese
Ao Chen | Maosong Sun
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

With the explosive growth of Internet, more and more domain-specific environments appear, such as forums, blogs, MOOCs and etc. Domain-specific words appear in these areas and always play a critical role in the domain-specific NLP tasks. This paper aims at extracting Chinese domain-specific new words automatically. The extraction of domain-specific new words has two parts including both new words in this domain and the especially important words. In this work, we propose a joint statistical model to perform these two works simultaneously. Compared to traditional new words detection models, our model doesn’t need handcraft features which are labor intensive. Experimental results demonstrate that our joint model achieves a better performance compared with the state-of-the-art methods.

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Neural Relation Extraction with Multi-lingual Attention
Yankai Lin | Zhiyuan Liu | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Relation extraction has been widely used for finding unknown relational facts from plain text. Most existing methods focus on exploiting mono-lingual data for relation extraction, ignoring massive information from the texts in various languages. To address this issue, we introduce a multi-lingual neural relation extraction framework, which employs mono-lingual attention to utilize the information within mono-lingual texts and further proposes cross-lingual attention to consider the information consistency and complementarity among cross-lingual texts. Experimental results on real-world datasets show that, our model can take advantage of multi-lingual texts and consistently achieve significant improvements on relation extraction as compared with baselines.

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Visualizing and Understanding Neural Machine Translation
Yanzhuo Ding | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework. We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors.

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Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
Jiacheng Zhang | Yang Liu | Huanbo Luan | Jingfang Xu | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge. In this work, we propose to use posterior regularization to provide a general framework for integrating prior knowledge into neural machine translation. We represent prior knowledge sources as features in a log-linear model, which guides the learning processing of the neural translation model. Experiments on Chinese-English dataset show that our approach leads to significant improvements.

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CANE: Context-Aware Network Embedding for Relation Modeling
Cunchao Tu | Han Liu | Zhiyuan Liu | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Network embedding (NE) is playing a critical role in network analysis, due to its ability to represent vertices with efficient low-dimensional embedding vectors. However, existing NE models aim to learn a fixed context-free embedding for each vertex and neglect the diverse roles when interacting with other vertices. In this paper, we assume that one vertex usually shows different aspects when interacting with different neighbor vertices, and should own different embeddings respectively. Therefore, we present Context-Aware Network Embedding (CANE), a novel NE model to address this issue. CANE learns context-aware embeddings for vertices with mutual attention mechanism and is expected to model the semantic relationships between vertices more precisely. In experiments, we compare our model with existing NE models on three real-world datasets. Experimental results show that CANE achieves significant improvement than state-of-the-art methods on link prediction and comparable performance on vertex classification. The source code and datasets can be obtained from https://github.com/thunlp/CANE.

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Adversarial Training for Unsupervised Bilingual Lexicon Induction
Meng Zhang | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Word embeddings are well known to capture linguistic regularities of the language on which they are trained. Researchers also observe that these regularities can transfer across languages. However, previous endeavors to connect separate monolingual word embeddings typically require cross-lingual signals as supervision, either in the form of parallel corpus or seed lexicon. In this work, we show that such cross-lingual connection can actually be established without any form of supervision. We achieve this end by formulating the problem as a natural adversarial game, and investigating techniques that are crucial to successful training. We carry out evaluation on the unsupervised bilingual lexicon induction task. Even though this task appears intrinsically cross-lingual, we are able to demonstrate encouraging performance without any cross-lingual clues.

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Improved Word Representation Learning with Sememes
Yilin Niu | Ruobing Xie | Zhiyuan Liu | Maosong Sun
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by several sememes. Since sememes are not explicit for each word, people manually annotate word sememes and form linguistic common-sense knowledge bases. In this paper, we present that, word sememe information can improve word representation learning (WRL), which maps words into a low-dimensional semantic space and serves as a fundamental step for many NLP tasks. The key idea is to utilize word sememes to capture exact meanings of a word within specific contexts accurately. More specifically, we follow the framework of Skip-gram and present three sememe-encoded models to learn representations of sememes, senses and words, where we apply the attention scheme to detect word senses in various contexts. We conduct experiments on two tasks including word similarity and word analogy, and our models significantly outperform baselines. The results indicate that WRL can benefit from sememes via the attention scheme, and also confirm our models being capable of correctly modeling sememe information.

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Incorporating Relation Paths in Neural Relation Extraction
Wenyuan Zeng | Yankai Lin | Zhiyuan Liu | Maosong Sun
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences containing both entities. In fact, there are also many sentences containing only one of the target entities, which also provide rich useful information but not yet employed by relation extraction. To address this issue, we build inference chains between two target entities via intermediate entities, and propose a path-based neural relation extraction model to encode the relational semantics from both direct sentences and inference chains. Experimental results on real-world datasets show that, our model can make full use of those sentences containing only one target entity, and achieves significant and consistent improvements on relation extraction as compared with strong baselines. The source code of this paper can be obtained from https://github.com/thunlp/PathNRE.

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Earth Mover’s Distance Minimization for Unsupervised Bilingual Lexicon Induction
Meng Zhang | Yang Liu | Huanbo Luan | Maosong Sun
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Cross-lingual natural language processing hinges on the premise that there exists invariance across languages. At the word level, researchers have identified such invariance in the word embedding semantic spaces of different languages. However, in order to connect the separate spaces, cross-lingual supervision encoded in parallel data is typically required. In this paper, we attempt to establish the cross-lingual connection without relying on any cross-lingual supervision. By viewing word embedding spaces as distributions, we propose to minimize their earth mover’s distance, a measure of divergence between distributions. We demonstrate the success on the unsupervised bilingual lexicon induction task. In addition, we reveal an interesting finding that the earth mover’s distance shows potential as a measure of language difference.

2016

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Neural Sentiment Classification with User and Product Attention
Huimin Chen | Maosong Sun | Cunchao Tu | Yankai Lin | Zhiyuan Liu
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora
Chunyang Liu | Yang Liu | Maosong Sun | Huanbo Luan | Heng Yu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Minimum Risk Training for Neural Machine Translation
Shiqi Shen | Yong Cheng | Zhongjun He | Wei He | Hua Wu | Maosong Sun | Yang Liu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Semi-Supervised Learning for Neural Machine Translation
Yong Cheng | Wei Xu | Zhongjun He | Wei He | Hua Wu | Maosong Sun | Yang Liu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Neural Relation Extraction with Selective Attention over Instances
Yankai Lin | Shiqi Shen | Zhiyuan Liu | Huanbo Luan | Maosong Sun
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover’s Distance Regularization
Meng Zhang | Yang Liu | Huanbo Luan | Yiqun Liu | Maosong Sun
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Being able to induce word translations from non-parallel data is often a prerequisite for cross-lingual processing in resource-scarce languages and domains. Previous endeavors typically simplify this task by imposing the one-to-one translation assumption, which is too strong to hold for natural languages. We remove this constraint by introducing the Earth Mover’s Distance into the training of bilingual word embeddings. In this way, we take advantage of its capability to handle multiple alternative word translations in a natural form of regularization. Our approach shows significant and consistent improvements across four language pairs. We also demonstrate that our approach is particularly preferable in resource-scarce settings as it only requires a minimal seed lexicon.

2015

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Modeling Relation Paths for Representation Learning of Knowledge Bases
Yankai Lin | Zhiyuan Liu | Huanbo Luan | Maosong Sun | Siwei Rao | Song Liu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Consistency-Aware Search for Word Alignment
Shiqi Shen | Yang Liu | Maosong Sun | Huanbo Luan
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Online Learning of Interpretable Word Embeddings
Hongyin Luo | Zhiyuan Liu | Huanbo Luan | Maosong Sun
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Generalized Agreement for Bidirectional Word Alignment
Chunyang Liu | Yang Liu | Maosong Sun | Huanbo Luan | Heng Yu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Learning Cross-lingual Word Embeddings via Matrix Co-factorization
Tianze Shi | Zhiyuan Liu | Yang Liu | Maosong Sun
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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A Neural Reordering Model for Phrase-based Translation
Peng Li | Yang Liu | Maosong Sun | Tatsuya Izuha | Dakun Zhang
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Query Lattice for Translation Retrieval
Meiping Dong | Yong Cheng | Yang Liu | Jia Xu | Maosong Sun | Tatsuya Izuha | Jie Hao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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A Unified Model for Word Sense Representation and Disambiguation
Xinxiong Chen | Zhiyuan Liu | Maosong Sun
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Recursive Autoencoders for ITG-Based Translation
Peng Li | Yang Liu | Maosong Sun
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Word Segmentation on Chinese Mirco-Blog Data with a Linear-Time Incremental Model
Kaixu Zhang | Maosong Sun | Changle Zhou
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Topical Word Trigger Model for Keyphrase Extraction
Zhiyuan Liu | Chen Liang | Maosong Sun
Proceedings of COLING 2012

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Random Walks on Context-Aware Relation Graphs for Ranking Social Tags
Han Li | Zhiyuan Liu | Maosong Sun
Proceedings of COLING 2012: Posters

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A Beam Search Algorithm for ITG Word Alignment
Peng Li | Yang Liu | Maosong Sun
Proceedings of COLING 2012: Posters

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Expert Finding for Microblog Misinformation Identification
Chen Liang | Zhiyuan Liu | Maosong Sun
Proceedings of COLING 2012: Posters

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Tag Dispatch Model with Social Network Regularization for Microblog User Tag Suggestion
Zhiyuan Liu | Cunchao Tu | Maosong Sun
Proceedings of COLING 2012: Posters

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THUTR: A Translation Retrieval System
Chunyang Liu | Qi Liu | Yang Liu | Maosong Sun
Proceedings of COLING 2012: Demonstration Papers

2011

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Automatic Keyphrase Extraction by Bridging Vocabulary Gap
Zhiyuan Liu | Xinxiong Chen | Yabin Zheng | Maosong Sun
Proceedings of the Fifteenth Conference on Computational Natural Language Learning

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A Simple Word Trigger Method for Social Tag Suggestion
Zhiyuan Liu | Xinxiong Chen | Maosong Sun
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Extract Chinese Unknown Words from a Large-scale Corpus Using Morphological and Distributional Evidences
Kaixu Zhang | Ruining Wang | Ping Xue | Maosong Sun
Proceedings of 5th International Joint Conference on Natural Language Processing

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Semi-Supervised SimHash for Efficient Document Similarity Search
Qixia Jiang | Maosong Sun
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Why Press Backspace? Understanding User Input Behaviors in Chinese Pinyin Input Method
Yabin Zheng | Lixing Xie | Zhiyuan Liu | Maosong Sun | Yang Zhang | Liyun Ru
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Automatic Keyphrase Extraction via Topic Decomposition
Zhiyuan Liu | Wenyi Huang | Yabin Zheng | Maosong Sun
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Explore the Structure of Social Tags by Subsumption Relations
Xiance Si | Zhiyuan Liu | Maosong Sun
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Fast-Champollion: A Fast and Robust Sentence Alignment Algorithm
Peng Li | Maosong Sun | Ping Xue
Coling 2010: Posters

2009

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Punctuation as Implicit Annotations for Chinese Word Segmentation
Zhongguo Li | Maosong Sun
Computational Linguistics, Volume 35, Number 4, December 2009

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Clustering to Find Exemplar Terms for Keyphrase Extraction
Zhiyuan Liu | Peng Li | Yabin Zheng | Maosong Sun
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Incorporate Web Search Technology to Solve Out-of-Vocabulary Words in Chinese Word Segmentation
Wei Qiao | Maosong Sun
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

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Word Segmentation Standard in Chinese, Japanese and Korean
Key-Sun Choi | Hitoshi Isahara | Kyoko Kanzaki | Hansaem Kim | Seok Mun Pak | Maosong Sun
Proceedings of the 7th Workshop on Asian Language Resources (ALR7)

2007

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Scalable Term Selection for Text Categorization
Jingyang Li | Maosong Sun
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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A Comparison and Semi-Quantitative Analysis of Words and Character-Bigrams as Features in Chinese Text Categorization
Jingyang Li | Maosong Sun | Xian Zhang
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

2005

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Automatic Image Annotation Using Maximum Entropy Model
Wei Li | Maosong Sun
Second International Joint Conference on Natural Language Processing: Full Papers

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A Method of Recognizing Entity and Relation
Xinghua Fan | Maosong Sun
Second International Joint Conference on Natural Language Processing: Full Papers

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Classifying Chinese Texts in Two Steps
Xinghua Fan | Maosong Sun | Key-sun Choi | Qin Zhang
Second International Joint Conference on Natural Language Processing: Full Papers

2003

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Two-Character Chinese Word Extraction Based on Hybrid of Internal and Contextual Measures
Shengfen Luo | Maosong Sun
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

2002

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Covering Ambiguity Resolution in Chinese Word Segmentation Based on Contextual Information
Xiao Luo | Maosong Sun | Benjamin K. Tsou
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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Identification of Chinese Personal Names in Unrestricted Texts
Lawrence Cheung | Benjamin K. Tsou | Maosong Sun
Proceedings of the 16th Pacific Asia Conference on Language, Information and Computation

2000

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Hua Yu: A Word-segmented and Part-Of-Speech Tagged Chinese Corpus
Maosong Sun | Honglin Sun | Changning Huang | Pu Zhang | Hongbing Xing | Qiang Zhou
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

1998

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Chinese Word Segmentation without Using Lexicon and Hand-crafted Training Data
Maosong Sun | Dayang Shen | Benjamin K. Tsou
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Chinese Word Segmentation without Using Lexicon and Hand-crafted Training Data
Maosong Sun | Dayang Shen | Benjamin K. Tsou
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

1997

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Chinese Word Segmentation and Part-of-Speech Tagging in One Step
Tom B.Y. Lai | Maosong Sun | Benjamin K. T’sou | S. Caesar Lun
ROCLING 1997 Poster Papers

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CSeg&Tagl.0: A Practical Word Segmenter and POS Tagger for Chinese Texts
Maosong Sun | Dayang Shen | Changning Huang
Fifth Conference on Applied Natural Language Processing

1995

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Ambiguity Resolution in Chinese Word Segmentation
Maosong Sun | Benjamin K. T’sou
Proceedings of the 10th Pacific Asia Conference on Language, Information and Computation

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