Yi Chang


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HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition
Zhiwei Yang | Jing Ma | Hechang Chen | Yunke Zhang | Yi Chang
Findings of the Association for Computational Linguistics: EMNLP 2021

Nested Named Entity Recognition (NNER) has been extensively studied, aiming to identify all nested entities from potential spans (i.e., one or more continuous tokens). However, recent studies for NNER either focus on tedious tagging schemas or utilize complex structures, which fail to learn effective span representations from the input sentence with highly nested entities. Intuitively, explicit span representations will contribute to NNER due to the rich context information they contain. In this study, we propose a Hierarchical Transformer (HiTRANS) network for the NNER task, which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner. Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network. Then a label prediction layer is designed to recognize nested entities hierarchically, which naturally explores semantic dependencies among different spans. Experiments on GENIA, ACE-2004, ACE-2005 and NNE datasets demonstrate that our proposed method achieves much better performance than the state-of-the-art approaches.

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Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction
Bo Wang | Tao Shen | Guodong Long | Tianyi Zhou | Yi Chang
Findings of the Association for Computational Linguistics: EMNLP 2021

Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence. ALSC is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect. To address this problem, recent works fine-tune pre-trained Transformer encoders for ALSC to extract an aspect-centric dependency tree that can locate the opinion words. However, the induced opinion words only provide an intuitive cue far below human-level interpretability. Besides, the pre-trained encoder tends to internalize an aspect’s intrinsic sentiment, causing sentiment bias and thus affecting model performance. In this paper, we propose a span-based anti-bias aspect representation learning framework. It first eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects’ prior sentiment. Then, it aligns the distilled opinion candidates with the aspect by span-based dependency modeling to highlight the interpretable opinion terms. Our method achieves new state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction.


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ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction
Erxin Yu | Wenjuan Han | Yuan Tian | Yi Chang
Proceedings of the 28th International Conference on Computational Linguistics

Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling problem and the long-tail problem. Most of the existing approaches address these two problems through flat classification, which lacks hierarchical information of relations. To leverage the informative relation hierarchies, we formulate DSRE as a hierarchical classification task and propose a novel hierarchical classification framework, which extracts the relation in a top-down manner. Specifically, in our proposed framework, 1) we use a hierarchically-refined representation method to achieve hierarchy-specific representation; 2) a top-down classification strategy is introduced instead of training a set of local classifiers. The experiments on NYT dataset demonstrate that our approach significantly outperforms other state-of-the-art approaches, especially for the long-tail problem.

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A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Zhepei Wei | Jianlin Su | Yue Wang | Yuan Tian | Yi Chang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple problem where multiple relational triples in the same sentence share the same entities. In this work, we introduce a fresh perspective to revisit the relational triple extraction task and propose a novel cascade binary tagging framework (CasRel) derived from a principled problem formulation. Instead of treating relations as discrete labels as in previous works, our new framework models relations as functions that map subjects to objects in a sentence, which naturally handles the overlapping problem. Experiments show that the CasRel framework already outperforms state-of-the-art methods even when its encoder module uses a randomly initialized BERT encoder, showing the power of the new tagging framework. It enjoys further performance boost when employing a pre-trained BERT encoder, outperforming the strongest baseline by 17.5 and 30.2 absolute gain in F1-score on two public datasets NYT and WebNLG, respectively. In-depth analysis on different scenarios of overlapping triples shows that the method delivers consistent performance gain across all these scenarios. The source code and data are released online.


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Zero-shot User Intent Detection via Capsule Neural Networks
Congying Xia | Chenwei Zhang | Xiaohui Yan | Yi Chang | Philip Yu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users’ utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: IntentCapsNet that extracts semantic features from utterances and aggregates them to discriminate existing intents, and IntentCapsNet-ZSL which gives IntentCapsNet the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available.

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Target-Sensitive Memory Networks for Aspect Sentiment Classification
Shuai Wang | Sahisnu Mazumder | Bing Liu | Mianwei Zhou | Yi Chang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. Given an aspect/target and a sentence, the task classifies the sentiment polarity expressed on the target in the sentence. Memory networks (MNs) have been used for this task recently and have achieved state-of-the-art results. In MNs, attention mechanism plays a crucial role in detecting the sentiment context for the given target. However, we found an important problem with the current MNs in performing the ASC task. Simply improving the attention mechanism will not solve it. The problem is referred to as target-sensitive sentiment, which means that the sentiment polarity of the (detected) context is dependent on the given target and it cannot be inferred from the context alone. To tackle this problem, we propose the target-sensitive memory networks (TMNs). Several alternative techniques are designed for the implementation of TMNs and their effectiveness is experimentally evaluated.

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Document Modeling with External Attention for Sentence Extraction
Shashi Narayan | Ronald Cardenas | Nikos Papasarantopoulos | Shay B. Cohen | Mirella Lapata | Jiangsheng Yu | Yi Chang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Document modeling is essential to a variety of natural language understanding tasks. We propose to use external information to improve document modeling for problems that can be framed as sentence extraction. We develop a framework composed of a hierarchical document encoder and an attention-based extractor with attention over external information. We evaluate our model on extractive document summarization (where the external information is image captions and the title of the document) and answer selection (where the external information is a question). We show that our model consistently outperforms strong baselines, in terms of both informativeness and fluency (for CNN document summarization) and achieves state-of-the-art results for answer selection on WikiQA and NewsQA.

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Abstract Meaning Representation for Paraphrase Detection
Fuad Issa | Marco Damonte | Shay B. Cohen | Xiaohui Yan | Yi Chang
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that naïve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6% for accuracy and 90.0% for F1 measure.


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Iterative Viterbi A* Algorithm for K-Best Sequential Decoding
Zhiheng Huang | Yi Chang | Bo Long | Jean-Francois Crespo | Anlei Dong | Sathiya Keerthi | Su-Lin Wu
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking
Jing Bai | Fernando Diaz | Yi Chang | Zhaohui Zheng | Keke Chen
Coling 2010: Posters

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Learning Recurrent Event Queries for Web Search
Ruiqiang Zhang | Yuki Konda | Anlei Dong | Pranam Kolari | Yi Chang | Zhaohui Zheng
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing


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Search Engine Adaptation by Feedback Control Adjustment for Time-sensitive Query
Ruiqiang Zhang | Yi Chang | Zhaohui Zheng | Donald Metzler | Jian-yun Nie
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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Empirical Exploitation of Click Data for Task Specific Ranking
Anlei Dong | Yi Chang | Shihao Ji | Ciya Liao | Xin Li | Zhaohui Zheng
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing


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Enhancing image-based Arabic document translation using noisy channel correction model
Yi Chang | Ying Zhang | Stephan Vogel | Jie Yang
Proceedings of Machine Translation Summit XI: Papers