Papers by Jianhuang Lai
Depth Growing for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition. |
| Approach: | They propose a two-stage approach with three specially designed components to construct deeper NMT models. |
| Outcome: | The proposed approach improves on WMT14 EnglishGerman and EnglishFrench translation tasks. |
A Study of Reinforcement Learning for Neural Machine Translation (D18-1)
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| Challenge: | Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation systems. |
| Approach: | They propose to leverage reinforcement learning to boost the performance of NMT systems trained with monolingual data. |
| Outcome: | The proposed method achieves competitive results on translation tasks in English-German, Chinese-English and English-English systems. |
Machine Translation With Weakly Paired Documents (D19-1)
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| Challenge: | Recent studies explore the possibility of unsupervised machine translation with monolingual data only. |
| Approach: | They propose a method to mine bilingual sentences from weakly paired documents . they use word distribution-level alignments to constrain word distributions of two weakly-paired documents. |
| Outcome: | The proposed method outperforms previous results on six translation tasks using weakly paired bilingual documents and a large number of bilingual sentences. |
Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter (D18-1)
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| Challenge: | Neural machine translation suffers from exposure bias and error propagation problem. |
| Approach: | They conduct a series of analyses to deeply understand the accuracy drop problem . they find that the left part of the translated sentence is often better than its right part . |
| Outcome: | The results show that the left part of the translated sentence is often better than its right part in left-to-right decoding models. |
Exploiting Monolingual Data at Scale for Neural Machine Translation (D19-1)
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| Challenge: | Neural machine translation (NMT) is a well-known and expensive task. |
| Approach: | They propose a method to use target-side monolingual data for neural machine translation and propose 'synthetic bitext' they propose generating synthetic bitext by translating monolingual into the other domain using models pretrained on genuine bitext. |
| Outcome: | The proposed approach achieves state-of-the-art results on WMT16, WMT17, WTM18 EnglishGerman translations and WTM19 GermanFrench translations. |