Zhuosheng Zhang

Ph.D. Student
Department of Computer Science and Engineering
Shanghai Jiao Tong University

Email: zhangzs@sjtu.edu.cn
Office: SEIEE Building 3-220

Profile

I am a Ph.D. student in the Department of Computer Science and Engineering at Shanghai Jiao Tong University, advised by Prof. Hai Zhao. Before that, I was an internship research fellow at NICT from 2019-2020, working with Prof. Rui Wang, Kehai Chen, Masao Utiyama, and Eiichiro Sumita. I received M.S. degree from Shanghai Jiao Tong University in 2020, and B.S. degree from Wuhan University in 2016.

My primary research interests are natural language processing and machine learning, with the long-term goal of building intelligent machine learning systems with the human-level language comprehension ability to assist humans in an effective, interpretable, and robust way (Survey). In pursuit of this goal, I develop principled methodologies of powering the deep neural networks with massive linguistic, commonsense, and multimodal knowledge, in support of real-world application scenarios such as question answering and multi-turn dialogue.

I have published more than 20 papers in top-tier NLP/ML/AI conferences and journals, including ACL, ICLR, AAAI, EMNLP, COLING, TPAMI, TKDE, and TASLP. I won 1st place in MRC leaderboards, such as SQuAD2.0, MuTual, RACE, ShARC, CCL-CMRC2017, etc.

News

Tutorial

Invited Talk

Experience

• Jun. 2019 - Jul. 2020, Internship Research Fellow
  NICT, Kyoto, Japan. Working with Prof. Rui Wang, Kehai Chen, Masao Utiyama, and Eiichiro Sumita.
• Jun. 2016 - Sep. 2016, Research Intern
  IBM CDL, Beijing, China. Advisor: Xinyu Wu

Education

• Sept. 2020 - Jun. 2023 (expected)
  Ph.D., Dept. of Computer Science and Engineering, Shanghai Jiao Tong University. Advisor: Prof. Hai Zhao
• Sept. 2016 - Mar. 2020
  M.S., Dept. of Computer Science and Engineering, Shanghai Jiao Tong University. Advisor: Prof. Hai Zhao
• Sept. 2012 - Jun. 2016
  B.S., Dept. of Computer Science and Engineering, Wuhan University. Advisor: Prof. Haojun Ai

Selected Publications

• Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond
  Zhuosheng Zhang, Hai Zhao, Rui Wang.
  [PDF] [Abstract] [Bib] [Slides]
  
  AwesomeMRC

  Machine reading comprehension (MRC) aims to teach machines to read and comprehend human languages, which is a long-standing goal of natural language processing (NLP). With the burst of deep neural networks and the evolution of contextualized language models (CLMs), the research of MRC has experienced two significant breakthroughs. MRC and CLM, as a phenomenon, have a great impact on the NLP community. In this survey, we provide a comprehensive and comparative review on MRC covering overall research topics about 1) the origin and development of MRC and CLM, with a particular focus on the role of CLMs; 2) the impact of MRC and CLM to the NLP community; 3) the definition, datasets, and evaluation of MRC; 4) general MRC architecture and technical methods in the view of two-stage Encoder-Decoder solving architecture from the insights of the cognitive process of humans; 5) previous highlights, emerging topics, and our empirical analysis, among which we especially focus on what works in different periods of MRC researches. We propose a full-view categorization and new taxonomies on these topics. The primary views we have arrived at are that 1) MRC boosts the progress from language processing to understanding; 2) the rapid improvement of MRC systems greatly benefits from the development of CLMs; 3) the theme of MRC is gradually moving from shallow text matching to cognitive reasoning.

  @article{zhang2020mrc,
    title={Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond},
    author={Zhang, Zhuosheng and Zhao, Hai and Wang, Rui},
    journal={arXiv preprint arXiv:2005.06249},
    year={2020}
  }
                

• Advances in Multi-turn Dialogue Comprehension: A Survey
  Zhuosheng Zhang, Hai Zhao.
  [PDF] [Abstract] [Bib]
  

  Training machines to understand natural language and interact with humans is an elusive and essential task in the field of artificial intelligence. In recent years, a diversity of dialogue systems has been designed with the rapid development of deep learning researches, especially the recent pre-trained language models. Among these studies, the fundamental yet challenging part is dialogue comprehension whose role is to teach the machines to read and comprehend the dialogue context before responding. In this paper, we review the previous methods from the perspective of dialogue modeling. We summarize the characteristics and challenges of dialogue comprehension in contrast to plain-text reading comprehension. Then, we discuss three typical patterns of dialogue modeling that are widely-used in dialogue comprehension tasks such as response selection and conversation question-answering, as well as dialogue-related language modeling techniques to enhance PrLMs in dialogue scenarios. Finally, we highlight the technical advances in recent years and point out the lessons we can learn from the empirical analysis and the prospects towards a new frontier of researches.

  @article{zhang2021advances,
    title={Advances in Multi-turn Dialogue Comprehension: A Survey},
    author={Zhang, Zhuosheng and Zhao, Hai},
    journal={arXiv preprint arXiv:2103.03125},
    year={2021}
  }
                

• Structural Pre-training for Dialogue Comprehension
  Zhuosheng Zhang, Hai Zhao.
  ACL, 2021. [PDF] [Abstract] [Bib]
  
  SPIDER

  Pre-trained language models (PrLMs) have demonstrated superior performance due to their strong ability to learn universal language representations from self-supervised pre-training. However, even with the help of the powerful PrLMs, it is still challenging to effectively capture task-related knowledge from dialogue texts which are enriched by correlations among speaker-aware utterances. In this work, we present SPIDER, Structural Pre-traIned DialoguE Reader, to capture dialogue exclusive features. To simulate the dialogue-like features, we propose two training objectives in addition to the original LM objectives: 1) utterance order restoration, which predicts the order of the permuted utterances in dialogue context; 2) sentence backbone regularization, which regularizes the model to improve the factual correctness of summarized subject-verb-object triplets. Experimental results on widely used dialogue benchmarks verify the effectiveness of the newly introduced self-supervised tasks.

  @inproceedings{zhang2021structural,
    title={Structural Pre-training for Dialogue Comprehension},
    author={Zhang, Zhuosheng and Zhao, Hai},
    booktitle={The 59th Annual Meeting of the Association for Computational Linguistics (ACL 2021)},
    year={2021}
  }
                

• Dialogue Graph Modeling for Conversational Machine Reading
  Siru Ouyang#, Zhuosheng Zhang#, Hai Zhao
  ACL (Findings), 2021. [PDF] [Abstract] [Bib]
  Ranking 1st on the ShARC leaderboard.
  
  DGM

  Conversational Machine Reading (CMR) aims at answering questions in complicated interactive scenarios. Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and even initiatively asks questions for clarification if necessary. Namely, the answer to the task needs a machine in the response of either \textsl{Yes, No, Irrelevant} or to raise a follow-up question for further clarification. To effectively capture multiple objects in such a challenging task, graph modeling is supposed to be adopted, though it is surprising that this does not happen until this work proposes a dialogue graph modeling framework by incorporating two complementary graph models, i.e., explicit discourse graph and implicit discourse graph, which respectively capture explicit and implicit interactions hidden in the rule documents. The proposed model is evaluated on the ShARC benchmark and achieves new state-of-the-art by first exceeding the milestone accuracy score of 80\%.

  @inproceedings{ouyang2021dialogue,
    title={Dialogue Graph Modeling for Conversational Machine Reading},
    author={Ouyang, Siru and Zhang, Zhuosheng and Zhao, Hai},
    booktitle={Findings of the Association for Computational Linguistics: ACL 2021},
    year={2021}
  }
                

• Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge
  Zhuosheng Zhang, Junlong Li, Hai Zhao.
  IEEE/ACM TASLP, 2021. [PDF] [Abstract] [Bib]
  

  Multi-turn dialogue reading comprehension aims to teach machines to read dialogue contexts and solve tasks such as response selection and answering questions. The major challenges involve noisy history contexts and especial prerequisites of commonsense knowledge that is unseen in the given material. Existing works mainly focus on context and response matching approaches. This work thus makes the first attempt to tackle the above two challenges by extracting substantially important turns as pivot utterances and utilizing external knowledge to enhance the representation of context. We propose a pivot-oriented deep selection model (PoDS) on top of the Transformer-based language models for dialogue comprehension. In detail, our model first picks out the pivot utterances from the conversation history according to the semantic matching with the candidate response or question, if any. Besides, knowledge items related to the dialogue context are extracted from a knowledge graph as external knowledge. Then, the pivot utterances and the external knowledge are combined together with a well-designed mechanism for refining predictions. Experimental results on four dialogue comprehension benchmark tasks show that our proposed model achieves great improvements on baselines. A series of empirical comparisons are conducted to show how our selection strategies and the extra knowledge injection influence the results.

  @article{zhang2021kkt,
    title={Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge},
    author={Zhang, Zhuosheng and Li, Junlong and Zhao, Hai},
    journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
    year={2021},
  }
                

• SG-Net: Syntax Guided Transformer for Language Representation
  Zhuosheng Zhang, Yuwei Wu, Junru Zhou, Sufeng Duan, Hai Zhao, Rui Wang.
  IEEE TPAMI, 2021. [PDF] [Abstract] [Bib]
  

  Understanding human language is one of the key themes of artificial intelligence. For language representation, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy texts and getting rid of the noises is essential to improve its performance. Traditional attentive models attend to all words without explicit constraint, which results in inaccurate concentration on some dispensable words. In this work, we propose using syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanisms for better linguistically motivated word representations. In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention. Syntax-guided network (SG-Net) is then composed of this extra SDOI-SAN and the SAN from the original Transformer encoder through a dual contextual architecture for better linguistics inspired representation. The proposed SG-Net is applied to typical Transformer encoders. Extensive experiments on popular benchmark tasks, including machine reading comprehension, natural language inference, and neural machine translation show the effectiveness of the proposed SG-Net design.

  @article{zhang2020sgnet,
    title={{SG-Net}: Syntax Guided Transformer for Language Representation},
    author={Zhang, Zhuosheng and Wu, Yuwei and Zhou, Junru and Duan, Sufeng and Zhao, Hai and Wang, Rui},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year={2020},
    doi={10.1109/TPAMI.2020.3046683},
    publisher={IEEE}
  }
                

• Text Compression-aided Transformer Encoding
  Zuchao Li, Zhuosheng Zhang, Hai Zhao, Rui Wang, Kehai Chen, Masao Utiyama, and Eiichiro Sumita.
  IEEE TPAMI, 2021. [PDF] [Abstract] [Bib]
  

  Text encoding is one of the most important steps in Natural Language Processing (NLP). It has been done well by the self-attention mechanism in the current state-of-the-art Transformer encoder, which has brought about significant improvements in the performance of many NLP tasks. Though the Transformer encoder may effectively capture general information in its resulting representations, the backbone information, meaning the gist of the input text, is not specifically focused on. In this paper, we propose explicit and implicit text compression approaches to enhance the Transformer encoding and evaluate models using this approach on several typical downstream tasks that rely on the encoding heavily. Our explicit text compression approaches use dedicated models to compress text, while our implicit text compression approach simply adds an additional module to the main model to handle text compression. We propose three ways of integration, namely backbone source-side fusion, target-side fusion, and both-side fusion, to integrate the backbone information into Transformer-based models for various downstream tasks. Our evaluation on benchmark datasets shows that the proposed explicit and implicit text compression approaches improve results in comparison to strong baselines. We therefore conclude, when comparing the encodings to the baseline models, text compression helps the encoders to learn better language representations.

  @article{li2021text,
    title={Text Compression-aided Transformer Encoding},
    author={Li, Zuchao and Zhang, Zhuosheng and Zhao, Hai and Wang, Rui and Chen, Kehai and Utiyama, Masao and Sumita, Eiichiro},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year={2021},
    publisher={IEEE}
  }
                

• Open Named Entity Modeling from Embedding Distribution
  Ying Luo#, Hai Zhao, Zhuosheng Zhang#, Bingjie Tang.
  IEEE TKDE, 2021. [PDF] [Abstract] [Bib]
  

  In this paper, we report our discovery on named entity distribution in general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from human labor and replies on schedule update. Our initial visualization of monolingual word embeddings indicates named entities tend to gather together despite of named entity types and language difference, which enable us to model all named entities using a specific geometric structure inside embedding space, namely, the named entity hypersphere. For monolingual case, the proposed named entity model gives an open description on diverse named entity types and different languages. For cross-lingual case, mapping the proposed named entity model provides a novel way to build named entity dataset for resource-poor languages. At last, the proposed named entity model may be shown as a very useful clue to significantly enhance state-of-the-art named entity recognition systems generally.

  @article{luo2021open,
    title={Open Named Entity Modeling from Embedding Distribution},
    author={Luo, Ying and Zhao, Hai and Zhang, Zhuosheng and Tang, Bingjie},
    journal={IEEE Transactions on Knowledge and Data Engineering},
    year={2021},
    doi={10.1109/TKDE.2021.3049654},
    publisher={IEEE}
  }
                

• Retrospective Reader for Machine Reading Comprehension
  Zhuosheng Zhang, Junjie Yang, Hai Zhao.
  AAAI, 2021. [PDF] [Abstract] [Bib]
  Ranking 1st on the SQuAD2.0 leaderboard.
  
  Retro-Reader

  Machine reading comprehension (MRC) is an AI challenge that requires machine to determine the correct answers to questions based on a given passage. MRC systems must not only answer question when necessary but also distinguish when no answer is available according to the given passage and then tactfully abstain from answering. When unanswerable questions are involved in the MRC task, an essential verification module called verifier is especially required in addition to the encoder, though the latest practice on MRC modeling still most benefits from adopting well pre-trained language models as the encoder block by only focusing on the "reading". This paper devotes itself to exploring better verifier design for the MRC task with unanswerable questions. Inspired by how humans solve reading comprehension questions, we proposed a retrospective reader (Retro-Reader) that integrates two stages of reading and verification strategies: 1) sketchy reading that briefly investigates the overall interactions of passage and question, and yield an initial judgment; 2) intensive reading that verifies the answer and gives the final prediction. The proposed reader is evaluated on two benchmark MRC challenge datasets SQuAD2.0 and NewsQA, achieving new state-of-the-art results. Significance tests show that our model is significantly better than the strong ELECTRA and ALBERT baselines. A series of analysis is also conducted to interpret the effectiveness of the proposed reader.

  @inproceedings{zhang2021retro,
    title={Retrospective Reader for Machine Reading Comprehension},
    author={Zhang, Zhuosheng and Yang, Junjie and Zhao, Hai},
    booktitle={The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)},
    year={2021}
  }
                

• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue
  Longxiang Liu#, Zhuosheng Zhang#, Hai Zhao, Xi Zhou, Xiang Zhou.
  AAAI, 2021. [PDF] [Abstract] [Bib]
  Ranking 1st on the MuTual leaderboard.
  
  MDFN

  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. Thus utterance- and speaker-aware clues are supposed to be well captured in models. However, in the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely by taking the pairwise dialogue history and candidate response as a whole, the hierarchical information on either utterance interrelation or speaker roles coupled in such representations is not well addressed. In this work, we propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history. In detail, we decouple the contextualized word representations by masking mechanisms in Transformer-based PrLM, making each word only focus on the words in current utterance, other utterances, two speaker roles (i.e., utterances of sender and utterances of receiver), respectively. Experimental results show that our method boosts the strong ELECTRA baseline substantially in four public benchmark datasets, and achieves various new state-of-the-art performance over previous methods. A series of ablation studies are conducted to demonstrate the effectiveness of our method.

  @inproceedings{liu2021filling,
    title={Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue},
    author={Liu, Longxiang and Zhang, Zhuosheng and and Zhao, Hai and Zhou, Xi and Zhou, Xiang},
    booktitle={The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)},
    year={2021}
  }
                

• Topic-Aware Multi-turn Dialogue Modeling
  Yi Xu, Hai Zhao, Zhuosheng Zhang
  AAAI, 2021. [PDF] [Abstract] [Bib]
  
  TADAM

  In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances. As a conversation goes on, topic shift at discourse-level naturally happens through the continuous multi-turn dialogue context. However, all known retrieval-based systems are satisfied with exploiting local topic words for context utterance representation but fail to capture such essential global topic-aware clues at discourse-level. Instead of taking topic-agnostic n-gram utterance as processing unit for matching purpose in existing systems, this paper presents a novel topic-aware solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way, so that the resulted model is capable of capturing salient topic shift at discourse-level in need and thus effectively track topic flow during multi-turn conversation. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network, which matches each topic segment with the response in a dual cross-attention way. Experimental results on three public datasets show TADAM can outperform the state-of-the-art method by a large margin, especially by 3.4% on E-commerce dataset that has an obvious topic shift.

  @inproceedings{xu2021topic,
    title={Topic-aware multi-turn dialogue modeling},
    author={Xu, Yi and Zhao, Hai and Zhang, Zhuosheng},
    booktitle={The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)},
    year={2021}
  }
                

• Multi-span Style Extraction for Generative Reading Comprehension
  Junjie Yang, Zhuosheng Zhang, Hai Zhao.
  AAAI 2021 Workshop on Scientific Document Understanding (SDU@AAAI-21). [PDF] [Abstract] [Bib]
  

  Generative machine reading comprehension (MRC) requires a model to generate well-formed answers. For this type of MRC, answer generation method is crucial to the model performance. However, generative models, which are supposed to be the right model for the task, in generally perform poorly. At the same time, single-span extraction models have been proven effective for extractive MRC, where the answer is constrained to a single span in the passage. Nevertheless, they generally suffer from generating incomplete answers or introducing redundant words when applied to the generative MRC. Thus, we extend the single-span extraction method to multi-span, proposing a new framework which enables generative MRC to be smoothly solved as multi-span extraction. Thorough experiments demonstrate that this novel approach can alleviate the dilemma between generative models and single-span models and produce answers with better-formed syntax and semantics. We will open-source our code for the research community.

  @inproceedings{Yang2021musst,
    title={Multi-span Style Extraction for Generative Reading Comprehension},
    author={Yang, Junjie and Zhang, Zhuosheng and Zhao, Hai},
    booktitle={The AAAI-21 Workshop on Scientific Document Understanding (SDU@AAAI-21)},
    year={2021}
  }
                

• Neural Machine Translation with Universal Visual Representation
  Zhuosheng Zhang, Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Zuchao Li, Hai Zhao.
  ICLR, 2020. [PDF] [Abstract] [Bib] [Video]
  Spotlight Oral, acceptance rate: 4.16%.
  
  UVR-NMT

  Though visual information has been introduced for enhancing neural machine translation (NMT), its effectiveness strongly relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations. In this paper, we present a universal visual representation learned over the monolingual corpora with image annotations, which overcomes the lack of large-scale bilingual sentence-image pairs, thereby extending image applicability in NMT. In detail, a group of images with similar topics to the source sentence will be retrieved from a light topic-image lookup table learned over the existing sentence-image pairs, and then is encoded as image representations by a pre-trained ResNet. An attention layer with a gated weighting is to fuse the visual information and text information as input to the decoder for predicting target translations. In particular, the proposed method enables the visual information to be integrated into large-scale text-only NMT in addition to the multimodel NMT. Experiments on four widely used translation datasets, including the WMT'16 English-to-Romanian, WMT'14 English-to-German, WMT'14 English-to-French, and Multi30K, show that the proposed approach achieves significant improvements over strong baselines.

  @inproceedings{zhang2020neural,
  title={Neural Machine Translation with Universal Visual Representation},
  author={Zhuosheng Zhang and Kehai Chen and Rui Wang and Masao Utiyama and Eiichiro Sumita and Zuchao Li and Hai Zhao},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=Byl8hhNYPS}
  }
                

• Data-dependent Gaussian Prior Objective for Language Generation
  Zuchao Li, Rui Wang, Kehai Chen, Masso Utiyama, Eiichiro Sumita, Zhuosheng Zhang, Hai Zhao.
  ICLR, 2020. [PDF]

• Semantics-aware BERT for Natural Language Understanding
  Zhuosheng Zhang, Yuwei Wu, Hai Zhao, Zuchao Li, Shuailiang Zhang, Xi Zhou, Xiang Zhou.
  AAAI, 2020. [PDF] [Abstract] [Bib]
  Ranking 1st on the SNLI leaderboard.
  
  SemBERT

  The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks. However, the existing language representation models including ELMo, GPT and BERT only exploit plain context-sensitive features such as character or word embeddings. They rarely consider incorporating structured semantic information which can provide rich semantics for language representation. To promote natural language understanding, we propose to incorporate explicit contextual semantics from pre-trained semantic role labeling, and introduce an improved language representation model, Semantics-aware BERT (SemBERT), which is capable of explicitly absorbing contextual semantics over a BERT backbone. SemBERT keeps the convenient usability of its BERT precursor in a light fine-tuning way without substantial task-specific modifications. Compared with BERT, semantics-aware BERT is as simple in concept but more powerful. It obtains new state-of-the-art or substantially improves results on ten reading comprehension and language inference tasks.

  @inproceedings{zhang2020semantics,
    title={Semantics-aware bert for language understanding},
    author={Zhang, Zhuosheng and Wu, Yuwei and Zhao, Hai and Li, Zuchao and Zhang, Shuailiang and Zhou, Xi and Zhou, Xiang},
    booktitle={Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020)},
    volume={34},
    number={05},
    pages={9628--9635},
    year={2020}
  }
  
                

• Syntax-Guided Machine Reading Comprehension
  Zhuosheng Zhang, Yuwei Wu, Junru Zhou, Sufeng Duan, Hai Zhao, Rui Wang.
  AAAI, 2020. [PDF] [Abstract] [Bib]
  The first single model to surpass human benchmark on the SQuAD2.0 leaderboard.
  
  SG-Net

  For machine reading comprehension, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy passages and getting ride of the noises is essential to improve its performance. Traditional attentive models attend to all words without explicit constraint, which results in inaccurate concentration on some dispensable words. In this work, we propose using syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanism for better linguistically motivated word representations. In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention. Syntax-guided network (SG-Net) is then composed of this extra SDOI-SAN and the SAN from the original Transformer encoder through a dual contextual architecture for better linguistics inspired representation. To verify its effectiveness, the proposed SG-Net is applied to typical pre-trained language model BERT which is right based on a Transformer encoder. Extensive experiments on popular benchmarks including SQuAD 2.0 and RACE show that the proposed SG-Net design helps achieve substantial performance improvement over strong baselines.

  @inproceedings{zhang2020sg,
    title={SG-Net: Syntax-Guided Machine Reading Comprehension.},
    author={Zhang, Zhuosheng and Wu, Yuwei and Zhou, Junru and Duan, Sufeng and Zhao, Hai and Wang, Rui},
    booktitle={Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020)},
    pages={9636--9643},
    year={2020}
  }
  
                

• DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension
  Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou.
  AAAI, 2020. [PDF] [Abstract] [Bib]
  Ranking 1st on the RACE leaderboard.
  
  DCMN