Ho-fung Leung


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IgSEG: Image-guided Story Ending Generation
Qingbao Huang | Chuan Huang | Linzhang Mo | Jielong Wei | Yi Cai | Ho-fung Leung | Qing Li
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Aligned Dual Channel Graph Convolutional Network for Visual Question Answering
Qingbao Huang | Jielong Wei | Yi Cai | Changmeng Zheng | Junying Chen | Ho-fung Leung | Qing Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Visual question answering aims to answer the natural language question about a given image. Existing graph-based methods only focus on the relations between objects in an image and neglect the importance of the syntactic dependency relations between words in a question. To simultaneously capture the relations between objects in an image and the syntactic dependency relations between words in a question, we propose a novel dual channel graph convolutional network (DC-GCN) for better combining visual and textual advantages. The DC-GCN model consists of three parts: an I-GCN module to capture the relations between objects in an image, a Q-GCN module to capture the syntactic dependency relations between words in a question, and an attention alignment module to align image representations and question representations. Experimental results show that our model achieves comparable performance with the state-of-the-art approaches.


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A Boundary-aware Neural Model for Nested Named Entity Recognition
Changmeng Zheng | Yi Cai | Jingyun Xu | Ho-fung Leung | Guandong Xu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In natural language processing, it is common that many entities contain other entities inside them. Most existing works on named entity recognition (NER) only deal with flat entities but ignore nested ones. We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels. Our model can locate entities precisely by detecting boundaries using sequence labeling models. Based on the detected boundaries, our model utilizes the boundary-relevant regions to predict entity categorical labels, which can decrease computation cost and relieve error propagation problem in layered sequence labeling model. We introduce multitask learning to capture the dependencies of entity boundaries and their categorical labels, which helps to improve the performance of identifying entities. We conduct our experiments on GENIA dataset and the experimental results demonstrate that our model outperforms other state-of-the-art methods.


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Exploring Topic Discriminating Power of Words in Latent Dirichlet Allocation
Kai Yang | Yi Cai | Zhenhong Chen | Ho-fung Leung | Raymond Lau
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Latent Dirichlet Allocation (LDA) and its variants have been widely used to discover latent topics in textual documents. However, some of topics generated by LDA may be noisy with irrelevant words scattering across these topics. We name this kind of words as topic-indiscriminate words, which tend to make topics more ambiguous and less interpretable by humans. In our work, we propose a new topic model named TWLDA, which assigns low weights to words with low topic discriminating power (ability). Our experimental results show that the proposed approach, which effectively reduces the number of topic-indiscriminate words in discovered topics, improves the effectiveness of LDA.