Yang Liu

May refer to several people

Other people with similar names: Yang Janet Liu (Georgetown University; 刘洋), Yang Liu (3M Health Information Systems), Yang Liu (University of Helsinki), Yang Liu (National University of Defense Technology), Yang Liu (Edinburgh), Yang Liu (The Chinese University of Hong Kong (Shenzhen)), Yang Liu (刘扬; Ph.D Purdue; ICSI, Dallas, Facebook, Liulishuo, Amazon), Yang Liu (刘洋; ICT, Tsinghua, Beijing Academy of Artificial Intelligence), Yang Liu (Microsoft Cognitive Services Research), Yang Liu (Peking University), Yang Liu (Univ. of Michigan, UC Santa Cruz)


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Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation
Hua Zheng | Damai Dai | Lei Li | Tianyu Liu | Zhifang Sui | Baobao Chang | Yang Liu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word. Most existing methods take the source word as an indecomposable semantic unit. However, in parataxis languages like Chinese, word meanings can be composed using the word formation process, where a word (“桃花”, peach-blossom) is formed by formation components (“桃”, peach; “花”, flower) using a formation rule (Modifier-Head). Inspired by this process, we propose to enhance DG with word formation features. We build a formation-informed dataset, and propose a model DeFT, which Decomposes words into formation features, dynamically Fuses different features through a gating mechanism, and generaTes word definitions. Experimental results show that our method is both effective and robust.

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Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts
Yang Liu | Yuanhe Tian | Tsung-Hui Chang | Song Wu | Xiang Wan | Yan Song
Proceedings of the 20th Workshop on Biomedical Language Processing

Chinese word segmentation (CWS) and medical concept recognition are two fundamental tasks to process Chinese electronic medical records (EMRs) and play important roles in downstream tasks for understanding Chinese EMRs. One challenge to these tasks is the lack of medical domain datasets with high-quality annotations, especially medical-related tags that reveal the characteristics of Chinese EMRs. In this paper, we collected a Chinese EMR corpus, namely, ACEMR, with human annotations for Chinese word segmentation and EMR-related tags. On the ACEMR corpus, we run well-known models (i.e., BiLSTM, BERT, and ZEN) and existing state-of-the-art systems (e.g., WMSeg and TwASP) for CWS and medical concept recognition. Experimental results demonstrate the necessity of building a dedicated medical dataset and show that models that leverage extra resources achieve the best performance for both tasks, which provides certain guidance for future studies on model selection in the medical domain.


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Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access
Seokhwan Kim | Mihail Eric | Karthik Gopalakrishnan | Behnam Hedayatnia | Yang Liu | Dilek Hakkani-Tur
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Most prior work on task-oriented dialogue systems are restricted to a limited coverage of domain APIs, while users oftentimes have domain related requests that are not covered by the APIs. In this paper, we propose to expand coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources. We define three sub-tasks: knowledge-seeking turn detection, knowledge selection, and knowledge-grounded response generation, which can be modeled individually or jointly. We introduce an augmented version of MultiWOZ 2.1, which includes new out-of-API-coverage turns and responses grounded on external knowledge sources. We present baselines for each sub-task using both conventional and neural approaches. Our experimental results demonstrate the need for further research in this direction to enable more informative conversational systems.

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On the Inference Calibration of Neural Machine Translation
Shuo Wang | Zhaopeng Tu | Shuming Shi | Yang Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Confidence calibration, which aims to make model predictions equal to the true correctness measures, is important for neural machine translation (NMT) because it is able to offer useful indicators of translation errors in the generated output. While prior studies have shown that NMT models trained with label smoothing are well-calibrated on the ground-truth training data, we find that miscalibration still remains a severe challenge for NMT during inference due to the discrepancy between training and inference. By carefully designing experiments on three language pairs, our work provides in-depth analyses of the correlation between calibration and translation performance as well as linguistic properties of miscalibration and reports a number of interesting findings that might help humans better analyze, understand and improve NMT models. Based on these observations, we further propose a new graduated label smoothing method that can improve both inference calibration and translation performance.

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A Hybrid System for NLPTEA-2020 CGED Shared Task
Meiyuan Fang | Kai Fu | Jiping Wang | Yang Liu | Jin Huang | Yitao Duan
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications

This paper introduces our system at NLPTEA2020 shared task for CGED, which is able to detect, locate, identify and correct grammatical errors in Chinese writings. The system consists of three components: GED, GEC, and post processing. GED is an ensemble of multiple BERT-based sequence labeling models for handling GED tasks. GEC performs error correction. We exploit a collection of heterogenous models, including Seq2Seq, GECToR and a candidate generation module to obtain correction candidates. Finally in the post processing stage, results from GED and GEC are fused to form the final outputs. We tune our models to lean towards optimizing precision, which we believe is more crucial in practice. As a result, among the six tracks in the shared task, our system performs well in the correction tracks: measured in F1 score, we rank first, with the highest precision, in the TOP3 correction track and third in the TOP1 correction track, also with the highest precision. Ours are among the top 4 to 6 in other tracks, except for FPR where we rank 12. And our system achieves the highest precisions among the top 10 submissions at IDENTIFICATION and POSITION tracks.


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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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Iterative Dual Domain Adaptation for Neural Machine Translation
Jiali Zeng | Yang Liu | Jinsong Su | Yubing Ge | Yaojie Lu | Yongjing Yin | Jiebo Luo
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model. In this paper, we argue that such a strategy fails to fully extract the domain-shared translation knowledge, and repeatedly utilizing corpora of different domains can lead to better distillation of domain-shared translation knowledge. To this end, we propose an iterative dual domain adaptation framework for NMT. Specifically, we first pretrain in-domain and out-of-domain NMT models using their own training corpora respectively, and then iteratively perform bidirectional translation knowledge transfer (from in-domain to out-of-domain and then vice versa) based on knowledge distillation until the in-domain NMT model convergences. Furthermore, we extend the proposed framework to the scenario of multiple out-of-domain training corpora, where the above-mentioned transfer is performed sequentially between the in-domain and each out-of-domain NMT models in the ascending order of their domain similarities. Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our framework.

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Text Summarization with Pretrained Encoders
Yang Liu | Mirella Lapata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework for both extractive and abstractive models. We introduce a novel document-level encoder based on BERT which is able to express the semantics of a document and obtain representations for its sentences. Our extractive model is built on top of this encoder by stacking several inter-sentence Transformer layers. For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not). We also demonstrate that a two-staged fine-tuning approach can further boost the quality of the generated summaries. Experiments on three datasets show that our model achieves state-of-the-art results across the board in both extractive and abstractive settings.

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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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Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis
Lishuang Li | Yang Liu | AnQiao Zhou
Proceedings of the 22nd Conference on Computational Natural Language Learning

Aspect-level sentiment analysis aims to identify the sentiment of a specific target in its context. Previous works have proved that the interactions between aspects and the contexts are important. On this basis, we also propose a succinct hierarchical attention based mechanism to fuse the information of targets and the contextual words. In addition, most existing methods ignore the position information of the aspect when encoding the sentence. In this paper, we argue that the position-aware representations are beneficial to this task. Therefore, we propose a hierarchical attention based position-aware network (HAPN), which introduces position embeddings to learn the position-aware representations of sentences and further generate the target-specific representations of contextual words. The experimental results on SemEval 2014 dataset show that our approach outperforms the state-of-the-art methods.


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The HIT-SCIR System for End-to-End Parsing of Universal Dependencies
Wanxiang Che | Jiang Guo | Yuxuan Wang | Bo Zheng | Huaipeng Zhao | Yang Liu | Dechuan Teng | Ting Liu
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

This paper describes our system (HIT-SCIR) for the CoNLL 2017 shared task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system includes three pipelined components: tokenization, Part-of-Speech (POS) tagging and dependency parsing. We use character-based bidirectional long short-term memory (LSTM) networks for both tokenization and POS tagging. Afterwards, we employ a list-based transition-based algorithm for general non-projective parsing and present an improved Stack-LSTM-based architecture for representing each transition state and making predictions. Furthermore, to parse low/zero-resource languages and cross-domain data, we use a model transfer approach to make effective use of existing resources. We demonstrate substantial gains against the UDPipe baseline, with an average improvement of 3.76% in LAS of all languages. And finally, we rank the 4th place on the official test sets.


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A Bilingual Discourse Corpus and Its Applications
Yang Liu | Jiajun Zhang | Chengqing Zong | Yating Yang | Xi Zhou
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Existing discourse research only focuses on the monolingual languages and the inconsistency between languages limits the power of the discourse theory in multilingual applications such as machine translation. To address this issue, we design and build a bilingual discource corpus in which we are currently defining and annotating the bilingual elementary discourse units (BEDUs). The BEDUs are then organized into hierarchical structures. Using this discourse style, we have annotated nearly 20K LDC sentences. Finally, we design a bilingual discourse based method for machine translation evaluation and show the effectiveness of our bilingual discourse annotations.


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Computing Semantic Text Similarity Using Rich Features
Yang Liu | Chengjie Sun | Lei Lin | Xiaolong Wang | Yuming Zhao
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

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yiGou: A Semantic Text Similarity Computing System Based on SVM
Yang Liu | Chengjie Sun | Lei Lin | Xiaolong Wang
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network
Qiao Qian | Bo Tian | Minlie Huang | Yang Liu | Xuan Zhu | Xiaoyan Zhu
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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The Discovery of Natural Typing Annotations: User-produced Potential Chinese Word Delimiters
Dakui Zhang | Yu Mao | Yang Liu | Hanshi Wang | Chuyuan Wei | Shiping Tang
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)


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Exploring Fine-grained Entity Type Constraints for Distantly Supervised Relation Extraction
Yang Liu | Kang Liu | Liheng Xu | Jun Zhao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


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Attribute Relation Extraction from Template-inconsistent Semi-structured Text by Leveraging Site-level Knowledge
Yang Liu | Fang Liu | Siwei Lai | Kang Liu | Guangyou Zhou | Jun Zhao
Proceedings of the Sixth International Joint Conference on Natural Language Processing


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Unsupervised Domain Adaptation for Joint Segmentation and POS-Tagging
Yang Liu | Yue Zhang
Proceedings of COLING 2012: Posters