Shijin Wang


2021

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Adversarial Training for Machine Reading Comprehension with Virtual Embeddings
Ziqing Yang | Yiming Cui | Chenglei Si | Wanxiang Che | Ting Liu | Shijin Wang | Guoping Hu
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

Adversarial training (AT) as a regularization method has proved its effectiveness on various tasks. Though there are successful applications of AT on some NLP tasks, the distinguishing characteristics of NLP tasks have not been exploited. In this paper, we aim to apply AT on machine reading comprehension (MRC) tasks. Furthermore, we adapt AT for MRC tasks by proposing a novel adversarial training method called PQAT that perturbs the embedding matrix instead of word vectors. To differentiate the roles of passages and questions, PQAT uses additional virtual P/Q-embedding matrices to gather the global perturbations of words from passages and questions separately. We test the method on a wide range of MRC tasks, including span-based extractive RC and multiple-choice RC. The results show that adversarial training is effective universally, and PQAT further improves the performance.

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IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation
Jiefu Gong | Xiao Hu | Wei Song | Ruiji Fu | Zhichao Sheng | Bo Zhu | Shijin Wang | Ting Liu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

Automated Essay Assessment (AEA) aims to judge students’ writing proficiency in an automatic way. This paper presents a Chinese AEA system IFlyEssayAssess (IFlyEA), targeting on evaluating essays written by native Chinese students from primary and junior schools. IFlyEA provides multi-level and multi-dimension analytical modules for essay assessment. It has state-of-the-art grammar level analysis techniques, and also integrates components for rhetoric and discourse level analysis, which are important for evaluating native speakers’ writing ability, but still challenging and less studied in previous work. Based on the comprehensive analysis, IFlyEA provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization. These services can benefit both teachers and students during the process of writing teaching and learning.

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Benchmarking Robustness of Machine Reading Comprehension Models
Chenglei Si | Ziqing Yang | Yiming Cui | Wentao Ma | Ting Liu | Shijin Wang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Dynamic Connected Networks for Chinese Spelling Check
Baoxin Wang | Wanxiang Che | Dayong Wu | Shijin Wang | Guoping Hu | Ting Liu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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CharBERT: Character-aware Pre-trained Language Model
Wentao Ma | Yiming Cui | Chenglei Si | Ting Liu | Shijin Wang | Guoping Hu
Proceedings of the 28th International Conference on Computational Linguistics

Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable. However, those methods split a word into subword units and make the representation incomplete and fragile.In this paper, we propose a character-aware pre-trained language model named CharBERT improving on the previous methods (such as BERT, RoBERTa) to tackle these problems. We first construct the contextual word embedding for each token from the sequential character representations, then fuse the representations of characters and the subword representations by a novel heterogeneous interaction module. We also propose a new pre-training task named NLM (Noisy LM) for unsupervised character representation learning. We evaluate our method on question answering, sequence labeling, and text classification tasks, both on the original datasets and adversarial misspelling test sets. The experimental results show that our method can significantly improve the performance and robustness of PLMs simultaneously.

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Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection
Zhiqiang Guo | Zhaoci Liu | Zhenhua Ling | Shijin Wang | Lingjing Jin | Yunxia Li
Proceedings of the 28th International Conference on Computational Linguistics

Data scarcity is always a constraint on analyzing speech transcriptions for automatic Alzheimer’s disease (AD) detection, especially when the subjects are non-English speakers. To deal with this issue, this paper first proposes a contrastive learning method to obtain effective representations for text classification based on monolingual embeddings of BERT. Furthermore, a cross-lingual data augmentation method is designed by building autoencoders to learn the text representations shared by both languages. Experiments on a Mandarin AD corpus show that the contrastive learning method can achieve better detection accuracy than conventional CNN-based and BERTbased methods. Our cross-lingual data augmentation method also outperforms other compared methods when using another English AD corpus for augmentation. Finally, a best detection accuracy of 81.6% is obtained by our proposed methods on the Mandarin AD corpus.

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A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
Yiming Cui | Ting Liu | Ziqing Yang | Zhipeng Chen | Wentao Ma | Wanxiang Che | Shijin Wang | Guoping Hu
Proceedings of the 28th International Conference on Computational Linguistics

Owing to the continuous efforts by the Chinese NLP community, more and more Chinese machine reading comprehension datasets become available. To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC). The proposed task aims to fill the right candidate sentence into the passage that has several blanks. We built a Chinese dataset called CMRC 2019 to evaluate the difficulty of the SC-MRC task. Moreover, to add more difficulties, we also made fake candidates that are similar to the correct ones, which requires the machine to judge their correctness in the context. The proposed dataset contains over 100K blanks (questions) within over 10K passages, which was originated from Chinese narrative stories. To evaluate the dataset, we implement several baseline systems based on the pre-trained models, and the results show that the state-of-the-art model still underperforms human performance by a large margin. We release the dataset and baseline system to further facilitate our community. Resources available through https://github.com/ymcui/cmrc2019

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Combining ResNet and Transformer for Chinese Grammatical Error Diagnosis
Shaolei Wang | Baoxin Wang | Jiefu Gong | Zhongyuan Wang | Xiao Hu | Xingyi Duan | Zizhuo Shen | Gang Yue | Ruiji Fu | Dayong Wu | Wanxiang Che | Shijin Wang | Guoping Hu | Ting Liu
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications

Grammatical error diagnosis is an important task in natural language processing. This paper introduces our system at NLPTEA-2020 Task: Chinese Grammatical Error Diagnosis (CGED). CGED aims to diagnose four types of grammatical errors which are missing words (M), redundant words (R), bad word selection (S) and disordered words (W). Our system is built on the model of multi-layer bidirectional transformer encoder and ResNet is integrated into the encoder to improve the performance. We also explore two ensemble strategies including weighted averaging and stepwise ensemble selection from libraries of models to improve the performance of single model. In official evaluation, our system obtains the highest F1 scores at identification level and position level. We also recommend error corrections for specific error types and achieve the second highest F1 score at correction level.

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Revisiting Pre-Trained Models for Chinese Natural Language Processing
Yiming Cui | Wanxiang Che | Ting Liu | Bing Qin | Shijin Wang | Guoping Hu
Findings of the Association for Computational Linguistics: EMNLP 2020

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models. In this paper, we target on revisiting Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pre-trained language model series to the community. We also propose a simple but effective model called MacBERT, which improves upon RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac). We carried out extensive experiments on eight Chinese NLP tasks to revisit the existing pre-trained language models as well as the proposed MacBERT. Experimental results show that MacBERT could achieve state-of-the-art performances on many NLP tasks, and we also ablate details with several findings that may help future research. https://github.com/ymcui/MacBERT

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Conversational Word Embedding for Retrieval-Based Dialog System
Wentao Ma | Yiming Cui | Ting Liu | Dong Wang | Shijin Wang | Guoping Hu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Human conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR-Embedding, which utilizes the conversation pairs <post, reply> to learn word embedding. Different from previous works, PR-Embedding uses the vectors from two different semantic spaces to represent the words in post and reply.To catch the information among the pair, we first introduce the word alignment model from statistical machine translation to generate the cross-sentence window, then train the embedding on word-level and sentence-level.We evaluate the method on single-turn and multi-turn response selection tasks for retrieval-based dialog systems.The experiment results show that PR-Embedding can improve the quality of the selected response.

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TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing
Ziqing Yang | Yiming Cui | Zhipeng Chen | Wanxiang Che | Ting Liu | Shijin Wang | Guoping Hu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing. It works with different neural network models and supports various kinds of supervised learning tasks, such as text classification, reading comprehension, sequence labeling. TextBrewer provides a simple and uniform workflow that enables quick setting up of distillation experiments with highly flexible configurations. It offers a set of predefined distillation methods and can be extended with custom code. As a case study, we use TextBrewer to distill BERT on several typical NLP tasks. With simple configurations, we achieve results that are comparable with or even higher than the public distilled BERT models with similar numbers of parameters.

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Is Graph Structure Necessary for Multi-hop Question Answering?
Nan Shao | Yiming Cui | Ting Liu | Shijin Wang | Guoping Hu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Recently, attempting to model texts as graph structure and introducing graph neural networks to deal with it has become a trend in many NLP research areas. In this paper, we investigate whether the graph structure is necessary for textual multi-hop reasoning. Our analysis is centered on HotpotQA. We construct a strong baseline model to establish that, with the proper use of pre-trained models, graph structure may not be necessary for textual multi-hop reasoning. We point out that both graph structure and adjacency matrix are task-related prior knowledge, and graph-attention can be considered as a special case of self-attention. Experiments demonstrate that graph-attention or the entire graph structure can be replaced by self-attention or Transformers.

2019

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Cross-Lingual Machine Reading Comprehension
Yiming Cui | Wanxiang Che | Ting Liu | Bing Qin | Shijin Wang | Guoping Hu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Though the community has made great progress on Machine Reading Comprehension (MRC) task, most of the previous works are solving English-based MRC problems, and there are few efforts on other languages mainly due to the lack of large-scale training data.In this paper, we propose Cross-Lingual Machine Reading Comprehension (CLMRC) task for the languages other than English. Firstly, we present several back-translation approaches for CLMRC task which is straightforward to adopt. However, to exactly align the answer into source language is difficult and could introduce additional noise. In this context, we propose a novel model called Dual BERT, which takes advantage of the large-scale training data provided by rich-resource language (such as English) and learn the semantic relations between the passage and question in bilingual context, and then utilize the learned knowledge to improve reading comprehension performance of low-resource language. We conduct experiments on two Chinese machine reading comprehension datasets CMRC 2018 and DRCD. The results show consistent and significant improvements over various state-of-the-art systems by a large margin, which demonstrate the potentials in CLMRC task. Resources available: https://github.com/ymcui/Cross-Lingual-MRC

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A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Yiming Cui | Ting Liu | Wanxiang Che | Li Xiao | Zhipeng Chen | Wentao Ma | Shijin Wang | Guoping Hu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in English. In this paper, we introduce a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated on Wikipedia paragraphs by human experts. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. We present several baseline systems as well as anonymous submissions for demonstrating the difficulties in this dataset. With the release of the dataset, we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We hope the release of the dataset could further accelerate the Chinese machine reading comprehension research. Resources are available: https://github.com/ymcui/cmrc2018

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IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis
Ziyue Wang | Baoxin Wang | Xingyi Duan | Dayong Wu | Shijin Wang | Guoping Hu | Ting Liu
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

Legal Tech is developed to help people with legal services and solve legal problems via machines. To achieve this, one of the key requirements for machines is to utilize legal knowledge and comprehend legal context. This can be fulfilled by natural language processing (NLP) techniques, for instance, text representation, text categorization, question answering (QA) and natural language inference, etc. To this end, we introduce a freely available Chinese Legal Tech system (IFlyLegal) that benefits from multiple NLP tasks. It is an integrated system that performs legal consulting, multi-way law searching, and legal document analysis by exploiting techniques such as deep contextual representations and various attention mechanisms. To our knowledge, IFlyLegal is the first Chinese legal system that employs up-to-date NLP techniques and caters for needs of different user groups, such as lawyers, judges, procurators, and clients. Since Jan, 2019, we have gathered 2,349 users and 28,238 page views (till June, 23, 2019).

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TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
Wentao Ma | Yiming Cui | Nan Shao | Su He | Wei-Nan Zhang | Ting Liu | Shijin Wang | Guoping Hu
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

We consider the importance of different utterances in the context for selecting the response usually depends on the current query. In this paper, we propose the model TripleNet to fully model the task with the triple <context, query, response> instead of <context, response > in previous works. The heart of TripleNet is a novel attention mechanism named triple attention to model the relationships within the triple at four levels. The new mechanism updates the representation of each element based on the attention with the other two concurrently and symmetrically.We match the triple <C, Q, R> centered on the response from char to context level for prediction.Experimental results on two large-scale multi-turn response selection datasets show that the proposed model can significantly outperform the state-of-the-art methods.

2018

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Dataset for the First Evaluation on Chinese Machine Reading Comprehension
Yiming Cui | Ting Liu | Zhipeng Chen | Wentao Ma | Shijin Wang | Guoping Hu
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Chinese Grammatical Error Diagnosis using Statistical and Prior Knowledge driven Features with Probabilistic Ensemble Enhancement
Ruiji Fu | Zhengqi Pei | Jiefu Gong | Wei Song | Dechuan Teng | Wanxiang Che | Shijin Wang | Guoping Hu | Ting Liu
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

This paper describes our system at NLPTEA-2018 Task #1: Chinese Grammatical Error Diagnosis. Grammatical Error Diagnosis is one of the most challenging NLP tasks, which is to locate grammar errors and tell error types. Our system is built on the model of bidirectional Long Short-Term Memory with a conditional random field layer (BiLSTM-CRF) but integrates with several new features. First, richer features are considered in the BiLSTM-CRF model; second, a probabilistic ensemble approach is adopted; third, Template Matcher are used during a post-processing to bring in human knowledge. In official evaluation, our system obtains the highest F1 scores at identifying error types and locating error positions, the second highest F1 score at sentence level error detection. We also recommend error corrections for specific error types and achieve the best F1 performance among all participants.

2017

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Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution
Ting Liu | Yiming Cui | Qingyu Yin | Wei-Nan Zhang | Shijin Wang | Guoping Hu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers. Therefore, the lack of annotated data becomes a major obstacle in the progress of zero pronoun resolution task. Also, it is expensive to spend manpower on labeling the data for better performance. To alleviate the problem above, in this paper, we propose a simple but novel approach to automatically generate large-scale pseudo training data for zero pronoun resolution. Furthermore, we successfully transfer the cloze-style reading comprehension neural network model into zero pronoun resolution task and propose a two-step training mechanism to overcome the gap between the pseudo training data and the real one. Experimental results show that the proposed approach significantly outperforms the state-of-the-art systems with an absolute improvements of 3.1% F-score on OntoNotes 5.0 data.

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Attention-over-Attention Neural Networks for Reading Comprehension
Yiming Cui | Zhipeng Chen | Si Wei | Shijin Wang | Ting Liu | Guoping Hu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Cloze-style reading comprehension is a representative problem in mining relationship between document and query. In this paper, we present a simple but novel model called attention-over-attention reader for better solving cloze-style reading comprehension task. The proposed model aims to place another attention mechanism over the document-level attention and induces “attended attention” for final answer predictions. One advantage of our model is that it is simpler than related works while giving excellent performance. In addition to the primary model, we also propose an N-best re-ranking strategy to double check the validity of the candidates and further improve the performance. Experimental results show that the proposed methods significantly outperform various state-of-the-art systems by a large margin in public datasets, such as CNN and Children’s Book Test.

2016

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LSTM Neural Reordering Feature for Statistical Machine Translation
Yiming Cui | Shijin Wang | Jianfeng Li
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Consensus Attention-based Neural Networks for Chinese Reading Comprehension
Yiming Cui | Ting Liu | Zhipeng Chen | Shijin Wang | Guoping Hu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Reading comprehension has embraced a booming in recent NLP research. Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension. In this work, we firstly present Chinese reading comprehension datasets, which consist of People Daily news dataset and Children’s Fairy Tale (CFT) dataset. Also, we propose a consensus attention-based neural network architecture to tackle the Cloze-style reading comprehension problem, which aims to induce a consensus attention over every words in the query. Experimental results show that the proposed neural network significantly outperforms the state-of-the-art baselines in several public datasets. Furthermore, we setup a baseline for Chinese reading comprehension task, and hopefully this would speed up the process for future research.

2014

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The USTC machine translation system for IWSLT 2014
Shijin Wang | Yuguang Wang | Jianfeng Li | Yiming Cui | Lirong Dai
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign