Jian Su


2024

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Humans Need Context, What about Machines? Investigating Conversational Context in Abusive Language Detection
Tom Bourgeade | Zongmin Li | Farah Benamara | Véronique Moriceau | Jian Su | Aixin Sun
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

A crucial aspect in abusive language on social media platforms (toxicity, hate speech, harmful stereotypes, etc.) is its inherent contextual nature. In this paper, we focus on the role of conversational context in abusive language detection, one of the most “direct” forms of context in this domain, as given by the conversation threads (e.g., directly preceding message, original post). The incorporation of surrounding messages has proven vital for the accurate human annotation of harmful content. However, many prior works have either ignored this aspect, collecting and processing messages in isolation, or have obtained inconsistent results when attempting to embed such contextual information into traditional classification methods. The reasons behind these findings have not yet been properly addressed. To this end, we propose an analysis of the impact of conversational context in abusive language detection, through: (1) an analysis of prior works and the limitations of the most common concatenation-based approach, which we attempt to address with two alternative architectures; (2) an evaluation of these methods on existing datasets in English, and a new dataset of French tweets annotated for hate speech and stereotypes; and (3) a qualitative analysis showcasing the necessity for context-awareness in ALD, but also its difficulties.

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Mitigating Linguistic Artifacts in Emotion Recognition for Conversations from TV Scripts to Daily Conversations
Donovan Ong | Shuo Sun | Jian Su | Bin Chen
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Emotion Recognition in Conversations (ERC) is a well-studied task with numerous potential real-world applications. However, existing ERC models trained on the MELD dataset derived from TV series, struggle when applied to daily conversation datasets. A closer examination of the datasets unveils the prevalence of linguistic artifacts such as repetitions and interjections in TV scripts, which ERC models may exploit when making predictions. To address this issue, we explore two techniques aimed at reducing the reliance of ERC models on these artifacts: 1) using contrastive learning to prioritize emotional features over dataset-specific linguistic style and 2) refining emotion predictions with pseudo-emotion intensity score. Our experiment results show that reducing reliance on the linguistic style found in TV transcripts could enhance model’s robustness and accuracy in diverse conversational contexts.

2023

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An Exploratory Study on Model Compression for Text-to-SQL
Shuo Sun | Yuze Gao | Yuchen Zhang | Jian Su | Bin Chen | Yingzhan Lin | Shuqi Sun
Findings of the Association for Computational Linguistics: ACL 2023

Text-to-SQL translates user queries into SQL statements that can retrieve relevant answers from relational databases. Recent approaches to Text-to-SQL rely on pre-trained language models that are computationally expensive and technically challenging to deploy in real-world applications that require real-time or on-device processing capabilities. In this paper, we perform a focused study on the feasibility of applying recent model compression techniques to sketch-based and sequence-to-sequence Text-to-SQL models. Our results reveal that sketch-based Text-to-SQL models generally have higher inference efficiency and respond better to model compression than sequence-to-sequence models, making them ideal for real-world deployments, especially in use cases with simple SQL statements.

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Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT - A Text-to-SQL Parsing Comparison
Shuo Sun | Yuchen Zhang | Jiahuan Yan | Yuze Gao | Donovan Ong | Bin Chen | Jian Su
Findings of the Association for Computational Linguistics: EMNLP 2023

The success of ChatGPT has ignited an AI race, with researchers striving to develop new large language models (LLMs) that can match or surpass the language understanding and generation abilities of commercial ones. In recent times, a number of models have emerged, claiming performance near that of GPT-3.5 or GPT-4 through various instruction-tuning methods. As practitioners of Text-to-SQL parsing, we are grateful for their valuable contributions to open-source research. However, it is important to approach these claims with a sense of scrutiny and ascertain the actual effectiveness of these models. Therefore, we pit six popular large language models against each other, systematically evaluating their Text-to-SQL parsing capability on nine benchmark datasets with five different prompting strategies, covering both zero-shot and few-shot scenarios. Regrettably, the open-sourced models fell significantly short of the performance achieved by closed-source models like GPT-3.5, highlighting the need for further work to bridge the performance gap between these models.

2018

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Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification
Yi Tay | Anh Tuan Luu | Siu Cheung Hui | Jian Su
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources. The key idea is that that incorporating a word-level prior can aid in the representation learning process, eventually improving model performance. To this end, our model employs two distinctly unique components, i.e., (1) we introduce a lexicon-driven contextual attention mechanism to imbue lexicon words with long-range contextual information and (2), we introduce a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. Via extensive experiments, we show that our approach outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets.

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Reasoning with Sarcasm by Reading In-Between
Yi Tay | Anh Tuan Luu | Siu Cheung Hui | Jian Su
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit. The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language. Sarcasm commonly manifests with a contrastive theme either between positive-negative sentiments or between literal-figurative scenarios. In this paper, we revisit the notion of modeling contrast in order to reason with sarcasm. More specifically, we propose an attention-based neural model that looks in-between instead of across, enabling it to explicitly model contrast and incongruity. We conduct extensive experiments on six benchmark datasets from Twitter, Reddit and the Internet Argument Corpus. Our proposed model not only achieves state-of-the-art performance on all datasets but also enjoys improved interpretability.

2016

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NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features
Zhiqiang Toh | Jian Su
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
Jian Su | Kevin Duh | Xavier Carreras
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction
Zhiqiang Toh | Jian Su
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Improving Twitter Named Entity Recognition using Word Representations
Zhiqiang Toh | Bin Chen | Jian Su
Proceedings of the Workshop on Noisy User-generated Text

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Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Lluís Màrquez | Chris Callison-Burch | Jian Su
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2013

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Exploiting Discourse Analysis for Article-Wide Temporal Classification
Jun-Ping Ng | Min-Yen Kan | Ziheng Lin | Wei Feng | Bin Chen | Jian Su | Chew-Lim Tan
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Learning a Replacement Model for Query Segmentation with Consistency in Search Logs
Wei Zhang | Yunbo Cao | Chin-Yew Lin | Jian Su | Chew-Lim Tan
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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A Lazy Learning Model for Entity Linking using Query-Specific Information
Wei Zhang | Jian Su | Chew-Lim Tan | Yunbo Cao | Chin-Yew Lin
Proceedings of COLING 2012

2011

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A Unified Event Coreference Resolution by Integrating Multiple Resolvers
Bin Chen | Jian Su | Sinno Jialin Pan | Chew Lim Tan
Proceedings of 5th International Joint Conference on Natural Language Processing

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A Wikipedia-LDA Model for Entity Linking with Batch Size Changing Instance Selection
Wei Zhang | Jian Su | Chew Lim Tan
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

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ECNU: Effective Semantic Relations Classification without Complicated Features or Multiple External Corpora
Yuan Chen | Man Lan | Jian Su | Zhi Min Zhou | Yu Xu
Proceedings of the 5th International Workshop on Semantic Evaluation

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Kernel Based Discourse Relation Recognition with Temporal Ordering Information
WenTing Wang | Jian Su | Chew Lim Tan
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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The Effects of Discourse Connectives Prediction on Implicit Discourse Relation Recognition
Zhi Min Zhou | Man Lan | Zheng Yu Niu | Yu Xu | Jian Su
Proceedings of the SIGDIAL 2010 Conference

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Resolving Event Noun Phrases to Their Verbal Mentions
Bin Chen | Jian Su | Chew Lim Tan
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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A Twin-Candidate Based Approach for Event Pronoun Resolution using Composite Kernel
Bin Chen | Jian Su | Chew Lim Tan
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Entity Linking Leveraging Automatically Generated Annotation
Wei Zhang | Jian Su | Chew Lim Tan | Wen Ting Wang
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Predicting Discourse Connectives for Implicit Discourse Relation Recognition
Zhi-Min Zhou | Yu Xu | Zheng-Yu Niu | Man Lan | Jian Su | Chew Lim Tan
Coling 2010: Posters

2009

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Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP
Keh-Yih Su | Jian Su | Janyce Wiebe | Haizhou Li
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Keh-Yih Su | Jian Su | Janyce Wiebe | Haizhou Li
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2008

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A Twin-Candidate Model for Learning-Based Anaphora Resolution
Xiaofeng Yang | Jian Su | Chew Lim Tan
Computational Linguistics, Volume 34, Number 3, September 2008

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Other-Anaphora Resolution in Biomedical Texts with Automatically Mined Patterns
Bin Chen | Xiaofeng Yang | Jian Su | Chew Lim Tan
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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An Effective Method of Using Web Based Information for Relation Extraction
Stanley Yong Wai Keong | Jian Su
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming
Xiaofeng Yang | Jian Su | Jun Lang | Chew Lim Tan | Ting Liu | Sheng Li
Proceedings of ACL-08: HLT

2007

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Coreference Resolution Using Semantic Relatedness Information from Automatically Discovered Patterns
Xiaofeng Yang | Jian Su
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge
Xiaofeng Yang | Jian Su | Chew Lim Tan
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Modeling Commonality among Related Classes in Relation Extraction
GuoDong Zhou | Jian Su | Min Zhang
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features
Min Zhang | Jie Zhang | Jian Su | GuoDong Zhou
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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A Phrase-Based Statistical Model for SMS Text Normalization
AiTi Aw | Min Zhang | Juan Xiao | Jian Su
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Exploring Syntactic Features for Relation Extraction using a Convolution Tree Kernel
Min Zhang | Jie Zhang | Jian Su
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2005

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Discovering Relations Between Named Entities from a Large Raw Corpus Using Tree Similarity-Based Clustering
Min Zhang | Jian Su | Danmei Wang | Guodong Zhou | Chew Lim Tan
Second International Joint Conference on Natural Language Processing: Full Papers

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A Phrase-Based Context-Dependent Joint Probability Model for Named Entity Translation
Min Zhang | Haizhou Li | Jian Su | Hendra Setiawan
Second International Joint Conference on Natural Language Processing: Full Papers

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A Twin-Candidate Model of Coreference Resolution with Non-Anaphor Identification Capability
Xiaofeng Yang | Jian Su | Chew Lim Tan
Second International Joint Conference on Natural Language Processing: Full Papers

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Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information
Xiaofeng Yang | Jian Su | Chew Lim Tan
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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Exploring Various Knowledge in Relation Extraction
GuoDong Zhou | Jian Su | Jie Zhang | Min Zhang
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

2004

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Improving Pronoun Resolution by Incorporating Coreferential Information of Candidates
Xiaofeng Yang | Jian Su | Guodong Zhou | Chew-Lim Tan
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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A Joint Source-Channel Model for Machine Transliteration
Haizhou Li | Min Zhang | Jian Su
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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Multi-Criteria-based Active Learning for Named Entity Recognition
Dan Shen | Jie Zhang | Jian Su | Guodong Zhou | Chew-Lim Tan
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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An NP-Cluster Based Approach to Coreference Resolution
Xiaofeng Yang | Jian Su | GuoDong Zhou | Chew Lim Tan
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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A High-Performance Coreference Resolution System using a Constraint-based Multi-Agent Strategy
GuoDong Zhou | Jian Su
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Direct Orthographical Mapping for Machine Transliteration
Min Zhang | Haizhou Li | Jian Su
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Exploring Deep Knowledge Resources in Biomedical Name Recognition
GuoDong Zhou | Jian Su
Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA/BioNLP)

2003

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Effective Adaptation of Hidden Markov Model-based Named Entity Recognizer for Biomedical Domain
Dan Shen | Jie Zhang | Guodong Zhou | Jian Su | Chew-Lim Tan
Proceedings of the ACL 2003 Workshop on Natural Language Processing in Biomedicine

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A Chinese Efficient Analyser Integrating Word Segmentation, Part-Of-Speech Tagging, Partial Parsing and Full Parsing
GuoDong Zhou | Jian Su
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

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Coreference Resolution Using Competition Learning Approach
Xiaofeng Yang | Guodong Zhou | Jian Su | Chew Lim Tan
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

2002

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Named Entity Recognition using an HMM-based Chunk Tagger
GuoDong Zhou | Jian Su
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

2000

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Hybrid Text Chunking
GuoDong Zhou | Jian Su | TongGuan Tey
Fourth Conference on Computational Natural Language Learning and the Second Learning Language in Logic Workshop

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Error-driven HMM-based Chunk Tagger with Context-dependent Lexicon
GuoDong Zhou | Jian Su
2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora