Hongxu Hou


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Multi-Reward based Reinforcement Learning for Neural Machine Translation
Shuo Sun | Hongxu Hou | Nier Wu | Ziyue Guo | Chaowei Zhang
Proceedings of the 19th Chinese National Conference on Computational Linguistics

Reinforcement learning (RL) has made remarkable progress in neural machine translation (NMT). However, it exists the problems with uneven sampling distribution, sparse rewards and high variance in training phase. Therefore, we propose a multi-reward reinforcement learning training strategy to decouple action selection and value estimation. Meanwhile, our method combines with language model rewards to jointly optimize model parameters. In addition, we add Gumbel noise in sampling to obtain more effective semantic information. To verify the robustness of our method, we not only conducted experiments on large corpora, but also performed on low-resource languages. Experimental results show that our work is superior to the baselines in WMT14 English-German, LDC2014 Chinese-English and CWMT2018 Mongolian-Chinese tasks, which fully certificates the effectiveness of our method.


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Improving Mongolian-Chinese Neural Machine Translation with Morphological Noise
Yatu Ji | Hongxu Hou | Chen Junjie | Nier Wu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

For the translation of agglutinative language such as typical Mongolian, unknown (UNK) words not only come from the quite restricted vocabulary, but also mostly from misunderstanding of the translation model to the morphological changes. In this study, we introduce a new adversarial training model to alleviate the UNK problem in Mongolian-Chinese machine translation. The training process can be described as three adversarial sub models (generator, value screener and discriminator), playing a win-win game. In this game, the added screener plays the role of emphasizing that the discriminator pays attention to the added Mongolian morphological noise in the form of pseudo-data and improving the training efficiency. The experimental results show that the newly emerged Mongolian-Chinese task is state-of-the-art. Under this premise, the training time is greatly shortened.


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Realignment from Finer-grained Alignment to Coarser-grained Alignment to Enhance Mongolian-Chinese SMT
Jing Wu | Hongxu Hou | Congjiao Xie
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters