|Appears in Collections:||Literature and Languages Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||A Novel Approach to Dropped Pronoun Translation|
|Citation:||Wang L, Tu Z, Zhang X, Li H, Way A & Liu Q (2016) A Novel Approach to Dropped Pronoun Translation Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, The 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2016), San Diego, USA, 12.6.2016 - 17.6.2016, Stroudsburg, PA, USA: The Association for Computational Linguistics, pp. 983-993.|
|Conference Name:||The 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2016)|
|Conference Location:||San Diego, USA|
|Abstract:||Dropped Pronouns (DP) in which pronouns are frequently dropped in the source language but should be retained in the target language are challenge in machine translation. In response to this problem, we propose a semisupervised approach to recall possibly missing pronouns in the translation. Firstly, we build training data for DP generation in which the DPs are automatically labelled according to the alignment information from a parallel corpus. Secondly, we build a deep learning-based DP generator for input sentences in decoding when no corresponding references exist. More specifically, the generation is two-phase: (1) DP position detection, which is modeled as a sequential labelling task with recurrent neural networks; and (2) DP prediction, which employs a multilayer perceptron with rich features. Finally, we integrate the above outputs into our translation system to recall missing pronouns by both extracting rules from the DP-labelled training data and translating the DP-generated input sentences. Experimental results show that our approach achieves a significant improvement of 1.58 BLEU points in translation performance with 66% F-score for DP generation accuracy.|
|Status:||Book Chapter: publisher version|
|Rights:||This paper is licensed under a Creative Commons Attribution 4.0 License.|
|Affiliation:||Dublin City University|
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