Papers by Max Meng
On the Word Alignment from Neural Machine Translation (P19-1)
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| Challenge: | Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, attention may fail to capture word alignment for some NMT models. |
| Approach: | They propose two methods to induce word alignment which are general and agnostic to specific NMT models. |
| Outcome: | The proposed methods induce much better word alignment than attention. |
Regularized Context Gates on Transformer for Machine Translation (2020.acl-main)
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| Challenge: | Context gates are effective to control the contributions from the source and target contexts in the recurrent neural network (RNN) based neural machine translation. |
| Approach: | They propose a method to identify source and target contexts and introduce a gate mechanism to control the contributions from source and targets in the advanced Transformer architecture. |
| Outcome: | The proposed model achieves an averaged gain of 1.0 BLEU score over a strong transformer baseline. |
Target Foresight Based Attention for Neural Machine Translation (N18-1)
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| Challenge: | Empirical experiments on Chinese-to-English and Japanese-to English datasets show that the proposed attention model delivers significant improvements in terms of alignment error rate and BLEU. |
| Approach: | They propose to explicitly access the target foresight word in the attention model to improve alignment and translation accuracy. |
| Outcome: | Empirical results show that the proposed model improves alignment error rate and BLEU on Chinese-to-English and Japanese-toEnglish datasets. |