Hai Leong Chieu


2020

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Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations
Jianfei Yu | Jing Jiang | Ling Min Serena Khoo | Hai Leong Chieu | Rui Xia
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The prevalent use of social media enables rapid spread of rumors on a massive scale, which leads to the emerging need of automatic rumor verification (RV). A number of previous studies focus on leveraging stance classification to enhance RV with multi-task learning (MTL) methods. However, most of these methods failed to employ pre-trained contextualized embeddings such as BERT, and did not exploit inter-task dependencies by using predicted stance labels to improve the RV task. Therefore, in this paper, to extend BERT to obtain thread representations, we first propose a Hierarchical Transformer, which divides each long thread into shorter subthreads, and employs BERT to separately represent each subthread, followed by a global Transformer layer to encode all the subthreads. We further propose a Coupled Transformer Module to capture the inter-task interactions and a Post-Level Attention layer to use the predicted stance labels for RV, respectively. Experiments on two benchmark datasets show the superiority of our Coupled Hierarchical Transformer model over existing MTL approaches.

2019

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Twitter Homophily: Network Based Prediction of User’s Occupation
Jiaqi Pan | Rishabh Bhardwaj | Wei Lu | Hai Leong Chieu | Xinghao Pan | Ni Yi Puay
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user’s occupational class. We show that the content information of a user’s tweets, the profile descriptions of a user’s follower/following community, and the user’s social network provide useful information for classifying a user’s occupational group. In our study, we extend an existing data set for this problem, and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work. In our analysis, we found that by using the graph convolutional network to exploit social homophily, we can achieve competitive performance on this data set with just a small fraction of the training data.

2017

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Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains
Liangguo Wang | Jing Jiang | Hai Leong Chieu | Chen Hui Ong | Dandan Song | Lejian Liao
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we study how to improve the domain adaptability of a deletion-based Long Short-Term Memory (LSTM) neural network model for sentence compression. We hypothesize that syntactic information helps in making such models more robust across domains. We propose two major changes to the model: using explicit syntactic features and introducing syntactic constraints through Integer Linear Programming (ILP). Our evaluation shows that the proposed model works better than the original model as well as a traditional non-neural-network-based model in a cross-domain setting.

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Universal Dependencies Parsing for Colloquial Singaporean English
Hongmin Wang | Yue Zhang | GuangYong Leonard Chan | Jie Yang | Hai Leong Chieu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.

2016

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A General Regularization Framework for Domain Adaptation
Wei Lu | Hai Leong Chieu | Jonathan Löfgren
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Learning to Capitalize with Character-Level Recurrent Neural Networks: An Empirical Study
Raymond Hendy Susanto | Hai Leong Chieu | Wei Lu
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2014

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Robust Domain Adaptation for Relation Extraction via Clustering Consistency
Minh Luan Nguyen | Ivor W. Tsang | Kian Ming A. Chai | Hai Leong Chieu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Domain Adaptation for Coreference Resolution: An Adaptive Ensemble Approach
Jian Bo Yang | Qi Mao | Qiao Liang Xiang | Ivor Wai-Hung Tsang | Kian Ming Adam Chai | Hai Leong Chieu
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Unsupervised Information Extraction with Distributional Prior Knowledge
Cane Wing-ki Leung | Jing Jiang | Kian Ming A. Chai | Hai Leong Chieu | Loo-Nin Teow
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Extracting Relation Descriptors with Conditional Random Fields
Yaliang Li | Jing Jiang | Hai Leong Chieu | Kian Ming A. Chai
Proceedings of 5th International Joint Conference on Natural Language Processing

2009

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Domain adaptive bootstrapping for named entity recognition
Dan Wu | Wee Sun Lee | Nan Ye | Hai Leong Chieu
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2003

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Named Entity Recognition with a Maximum Entropy Approach
Hai Leong Chieu | Hwee Tou Ng
Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003

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Closing the Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods
Hai Leong Chieu | Hwee Tou Ng | Yoong Keok Lee
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

2002

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Teaching a Weaker Classifier: Named Entity Recognition on Upper Case Text
Hai Leong Chieu | Hwee Tou Ng
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Named Entity Recognition: A Maximum Entropy Approach Using Global Information
Hai Leong Chieu | Hwee Tou Ng
COLING 2002: The 19th International Conference on Computational Linguistics