Zhenghao Liu


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CCL23-Eval 任务7总结报告: 汉语学习者文本纠错(Overview of CCL23-Eval Task: Chinese Learner Text Correction)
Hongxiang Chang | Yang Liu | Meng Xu | Yingying Wang | Cunliang Kong | Liner Yang | Yang Erhong | Gaoqi Sun, Maosong andRao | Renfen Hu | Zhenghao Liu | 鸿翔 常 | 洋 刘 | 萌 徐 | 莹莹 王 | 存良 孔 | 麟儿 杨 | 尔弘 杨 | 茂松 孙 | 高琦 饶 | 韧奋 胡 | 正皓 刘
Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)

“汉语学习者文本纠错(Chinese Learner Text Correction)评测比赛,是依托于第22届中国计算语言学大会举办的技术评测。针对汉语学习者文本,设置了多维度汉语学习者文本纠错和中文语法错误检测两个赛道。结合人工智能技术的不断进步和发展的时代背景,在两赛道下分别设置开放和封闭任务。开放任务允许使用大模型。以汉语学习者文本多维标注语料库YACLC为基础建设评测数据集,建立基于多参考答案的评价标准,构建基准评测框架,进一步推动汉语学习者文本纠错研究的发展。共38支队伍报名参赛,其中5支队伍成绩优异并提交了技术报告。”

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Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
Xinze Li | Zhenghao Liu | Chenyan Xiong | Shi Yu | Yu Gu | Zhiyuan Liu | Ge Yu
Findings of the Association for Computational Linguistics: ACL 2023

This paper presents Structure Aware Dense Retrieval (SANTA) model, which encodes user queries and structured data in one universal embedding space for retrieving structured data. SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) Structured Data Alignment, which utilizes the natural alignment relations between structured data and unstructured data for structure-aware pretraining. It contrastively trains language models to represent multi-modal text data and teaches models to distinguish matched structured data for unstructured texts. 2) Masked Entity Prediction, which designs an entity-oriented mask strategy and asks language models to fill in the masked entities. Our experiments show that SANTA achieves state-of-the-art on code search and product search and conducts convincing results in the zero-shot setting. SANTA learns tailored representations for multi-modal text data by aligning structured and unstructured data pairs and capturing structural semantics by masking and predicting entities in the structured data. All codes are available at https://github.com/OpenMatch/OpenMatch.

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Leveraging Prefix Transfer for Multi-Intent Text Revision
Ruining Chong | Cunliang Kong | Liu Wu | Zhenghao Liu | Ziye Jin | Liner Yang | Yange Fan | Hanghang Fan | Erhong Yang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Text revision is a necessary process to improve text quality. During this process, writers constantly edit texts out of different edit intentions. Identifying edit intention for a raw text is always an ambiguous work, and most previous work on revision systems mainly focuses on editing texts according to one specific edit intention. In this work, we aim to build a multi-intent text revision system that could revise texts without explicit intent annotation. Our system is based on prefix-tuning, which first gets prefixes for every edit intent, and then trains a prefix transfer module, enabling the system to selectively leverage the knowledge from various prefixes according to the input text. We conduct experiments on the IteraTeR dataset, and the results show that our system outperforms baselines. The system can significantly improve the SARI score with more than 3% improvements, which thrives on the learned editing intention prefixes.


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Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder
Zhenghao Liu | Han Zhang | Chenyan Xiong | Zhiyuan Liu | Yu Gu | Xiaohua Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Dense retrievers encode queries and documents and map them in an embedding space using pre-trained language models. These embeddings need to be high-dimensional to fit training signals and guarantee the retrieval effectiveness of dense retrievers. However, these high-dimensional embeddings lead to larger index storage and higher retrieval latency. To reduce the embedding dimensions of dense retrieval, this paper proposes a Conditional Autoencoder (ConAE) to compress the high-dimensional embeddings to maintain the same embedding distribution and better recover the ranking features. Our experiments show that ConAE is effective in compressing embeddings by achieving comparable ranking performance with its teacher model and making the retrieval system more efficient. Our further analyses show that ConAE can alleviate the redundancy of the embeddings of dense retrieval with only one linear layer. All codes of this work are available at https://github.com/NEUIR/ConAE.


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TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling
Huiyuan Xie | Zhenghao Liu | Chenyan Xiong | Zhiyuan Liu | Ann Copestake
Findings of the Association for Computational Linguistics: EMNLP 2021

Human conversations naturally evolve around different topics and fluently move between them. In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored. In this paper we introduce TIAGE, a new topic-shift aware dialog benchmark constructed utilizing human annotations on topic shifts. Based on TIAGE, we introduce three tasks to investigate different scenarios of topic-shift modeling in dialog settings: topic-shift detection, topic-shift triggered response generation and topic-aware dialog generation. Experiments on these tasks show that the topic-shift signals in TIAGE are useful for topic-shift response generation. On the other hand, dialog systems still struggle to decide when to change topic. This indicates further research is needed in topic-shift aware dialog modeling.

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Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
Si Sun | Yingzhuo Qian | Zhenghao Liu | Chenyan Xiong | Kaitao Zhang | Jie Bao | Zhiyuan Liu | Paul Bennett
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)

The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios. To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. Drawing on source-domain massive relevance supervision, MetaAdaptRank contrastively synthesizes a large number of weak supervision signals for target domains and meta-learns to reweight these synthetic “weak” data based on their benefits to the target-domain ranking accuracy of Neu-IR models. Experiments on three TREC benchmarks in the web, news, and biomedical domains show that MetaAdaptRank significantly improves the few-shot ranking accuracy of Neu-IR models. Further analyses indicate that MetaAdaptRank thrives from both its contrastive weak data synthesis and meta-reweighted data selection. The code and data of this paper can be obtained from https://github.com/thunlp/MetaAdaptRank.

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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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Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs
Houyu Zhang | Zhenghao Liu | Chenyan Xiong | Zhiyuan Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Human conversations naturally evolve around related concepts and hop to distant concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowledge graphs to explicitly model conversation flows. By grounding conversations to the concept space, ConceptFlow represents the potential conversation flow as traverses in the concept space along commonsense relations. The traverse is guided by graph attentions in the concept graph, moving towards more meaningful directions in the concept space, in order to generate more semantic and informative responses. Experiments on Reddit conversations demonstrate ConceptFlow’s effectiveness over previous knowledge-aware conversation models and GPT-2 based models while using 70% fewer parameters, confirming the advantage of explicit modeling conversation structures. All source codes of this work are available at https://github.com/thunlp/ConceptFlow.

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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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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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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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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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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.