Yik-Cheung Tam


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Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation
Zhiqiang Liu | Zuohui Fu | Jie Cao | Gerard de Melo | Yik-Cheung Tam | Cheng Niu | Jie Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Rhetoric is a vital element in modern poetry, and plays an essential role in improving its aesthetics. However, to date, it has not been considered in research on automatic poetry generation. In this paper, we propose a rhetorically controlled encoder-decoder for modern Chinese poetry generation. Our model relies on a continuous latent variable as a rhetoric controller to capture various rhetorical patterns in an encoder, and then incorporates rhetoric-based mixtures while generating modern Chinese poetry. For metaphor and personification, an automated evaluation shows that our model outperforms state-of-the-art baselines by a substantial margin, while human evaluation shows that our model generates better poems than baseline methods in terms of fluency, coherence, meaningfulness, and rhetorical aesthetics.


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Read and Comprehend by Gated-Attention Reader with More Belief
Haohui Deng | Yik-Cheung Tam
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

Gated-Attention (GA) Reader has been effective for reading comprehension. GA Reader makes two assumptions: (1) a uni-directional attention that uses an input query to gate token encodings of a document; (2) encoding at the cloze position of an input query is considered for answer prediction. In this paper, we propose Collaborative Gating (CG) and Self-Belief Aggregation (SBA) to address the above assumptions respectively. In CG, we first use an input document to gate token encodings of an input query so that the influence of irrelevant query tokens may be reduced. Then the filtered query is used to gate token encodings of an document in a collaborative fashion. In SBA, we conjecture that query tokens other than the cloze token may be informative for answer prediction. We apply self-attention to link the cloze token with other tokens in a query so that the importance of query tokens with respect to the cloze position are weighted. Then their evidences are weighted, propagated and aggregated for better reading comprehension. Experiments show that our approaches advance the state-of-theart results in CNN, Daily Mail, and Who Did What public test sets.


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Morphological Modeling for Machine Translation of English-Iraqi Arabic Spoken Dialogs
Katrin Kirchhoff | Yik-Cheung Tam | Colleen Richey | Wen Wang
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Bilingual-LSA Based LM Adaptation for Spoken Language Translation
Yik-Cheung Tam | Ian Lane | Tanja Schultz
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics


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PLASER: Pronunciation Learning via Automatic Speech Recognition
Brian Mak | Manhung Siu | Mimi Ng | Yik-Cheung Tam | Yu-Chung Chan | Kin-Wah Chan | Ka-Yee Leung | Simon Ho | Jimmy Wong | Jacqueline Lo
Proceedings of the HLT-NAACL 03 Workshop on Building Educational Applications Using Natural Language Processing