Sen Yang


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Investigating Non-local Features for Neural Constituency Parsing
Leyang Cui | Sen Yang | Yue Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been shown that non-local features in CRF structures lead to improvements. In this paper, we investigate injecting non-local features into the training process of a local span-based parser, by predicting constituent n-gram non-local patterns and ensuring consistency between non-local patterns and local constituents. Results show that our simple method gives better results than the self-attentive parser on both PTB and CTB. Besides, our method achieves state-of-the-art BERT-based performance on PTB (95.92 F1) and strong performance on CTB (92.31 F1). Our parser also outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings.

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Challenges to Open-Domain Constituency Parsing
Sen Yang | Leyang Cui | Ruoxi Ning | Di Wu | Yue Zhang
Findings of the Association for Computational Linguistics: ACL 2022

Neural constituency parsers have reached practical performance on news-domain benchmarks. However, their generalization ability to other domains remains weak. Existing findings on cross-domain constituency parsing are only made on a limited number of domains. Tracking this, we manually annotate a high-quality constituency treebank containing five domains. We analyze challenges to open-domain constituency parsing using a set of linguistic features on various strong constituency parsers. Primarily, we find that 1) BERT significantly increases parsers’ cross-domain performance by reducing their sensitivity on the domain-variant features.2) Compared with single metrics such as unigram distribution and OOV rate, challenges to open-domain constituency parsing arise from complex features, including cross-domain lexical and constituent structure variations.


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Template-Based Named Entity Recognition Using BART
Leyang Cui | Yu Wu | Jian Liu | Sen Yang | Yue Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Diversity and Consistency: Exploring Visual Question-Answer Pair Generation
Sen Yang | Qingyu Zhou | Dawei Feng | Yang Liu | Chao Li | Yunbo Cao | Dongsheng Li
Findings of the Association for Computational Linguistics: EMNLP 2021

Although showing promising values to downstream applications, generating question and answer together is under-explored. In this paper, we introduce a novel task that targets question-answer pair generation from visual images. It requires not only generating diverse question-answer pairs but also keeping the consistency of them. We study different generation paradigms for this task and propose three models: the pipeline model, the joint model, and the sequential model. We integrate variational inference into these models to achieve diversity and consistency. We also propose region representation scaling and attention alignment to improve the consistency further. We finally devise an evaluator as a quantitative metric for consistency. We validate our approach on two benchmarks, VQA2.0 and Visual-7w, by automatically and manually evaluating diversity and consistency. Experimental results show the effectiveness of our models: they can generate diverse or consistent pairs. Moreover, this task can be used to improve visual question generation and visual question answering.


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Making the Best Use of Review Summary for Sentiment Analysis
Sen Yang | Leyang Cui | Jun Xie | Yue Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment analysis. In this paper, we conduct a study to exploit methods for better use of summary information. We start by finding out that the sentimental signal distribution of a review and that of its corresponding summary are in fact complementary to each other. We thus explore various architectures to better guide the interactions between the two and propose a hierarchically-refined review-centric attention model. Empirical results show that our review-centric model can make better use of user-written summaries for review sentiment analysis, and is also more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.

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What Have We Achieved on Text Summarization?
Dandan Huang | Leyang Cui | Sen Yang | Guangsheng Bao | Kun Wang | Jun Xie | Yue Zhang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Deep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years. However, gaps still exist between summaries produced by automatic summarizers and human professionals. Aiming to gain more understanding of summarization systems with respect to their strengths and limits on a fine-grained syntactic and semantic level, we consult the Multidimensional Quality Metric (MQM) and quantify 8 major sources of errors on 10 representative summarization models manually. Primarily, we find that 1) under similar settings, extractive summarizers are in general better than their abstractive counterparts thanks to strength in faithfulness and factual-consistency; 2) milestone techniques such as copy, coverage and hybrid extractive/abstractive methods do bring specific improvements but also demonstrate limitations; 3) pre-training techniques, and in particular sequence-to-sequence pre-training, are highly effective for improving text summarization, with BART giving the best results.


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Exploring Pre-trained Language Models for Event Extraction and Generation
Sen Yang | Dawei Feng | Linbo Qiao | Zhigang Kan | Dongsheng Li
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Traditional approaches to the task of ACE event extraction usually depend on manually annotated data, which is often laborious to create and limited in size. Therefore, in addition to the difficulty of event extraction itself, insufficient training data hinders the learning process as well. To promote event extraction, we first propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. Moreover, to address the problem of insufficient training data, we propose a method to automatically generate labeled data by editing prototypes and screen out generated samples by ranking the quality. Experiments on the ACE2005 dataset demonstrate that our extraction model can surpass most existing extraction methods. Besides, incorporating our generation method exhibits further significant improvement. It obtains new state-of-the-art results on the event extraction task, including pushing the F1 score of trigger classification to 81.1%, and the F1 score of argument classification to 58.9%.


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Topic-Based Chinese Message Polarity Classification System at SIGHAN8-Task2
Chun Liao | Chong Feng | Sen Yang | Heyan Huang
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing