Xiaomian Kang


Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning
Xiaomian Kang | Yang Zhao | Jiajun Zhang | Chengqing Zong
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Document-level neural machine translation has yielded attractive improvements. However, majority of existing methods roughly use all context sentences in a fixed scope. They neglect the fact that different source sentences need different sizes of context. To address this problem, we propose an effective approach to select dynamic context so that the document-level translation model can utilize the more useful selected context sentences to produce better translations. Specifically, we introduce a selection module that is independent of the translation module to score each candidate context sentence. Then, we propose two strategies to explicitly select a variable number of context sentences and feed them into the translation module. We train the two modules end-to-end via reinforcement learning. A novel reward is proposed to encourage the selection and utilization of dynamic context sentences. Experiments demonstrate that our approach can select adaptive context sentences for different source sentences, and significantly improves the performance of document-level translation methods.


An End-to-End Chinese Discourse Parser with Adaptation to Explicit and Non-explicit Relation Recognition
Xiaomian Kang | Haoran Li | Long Zhou | Jiajun Zhang | Chengqing Zong
Proceedings of the CoNLL-16 shared task