Explicit Reordering for Neural Machine Translation

• URL: http://arxiv.org/abs/2004.03818v1
• Date: Wed, 8 Apr 2020 05:28:46 GMT
• Title: Explicit Reordering for Neural Machine Translation
• Authors: Kehai Chen, Rui Wang, Masao Utiyama, and Eiichiro Sumita
• Abstract summary: In Transformer-based neural machine translation (NMT), the positional encoding mechanism helps the self-attention networks to learn the source representation with order dependency. We propose a novel reordering method to explicitly model this reordering information for the Transformer-based NMT. The empirical results on the WMT14 English-to-German, WAT ASPEC Japanese-to-English, and WMT17 Chinese-to-English translation tasks show the effectiveness of the proposed approach.
• Score: 50.70683739103066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Transformer-based neural machine translation (NMT), the positional encoding mechanism helps the self-attention networks to learn the source representation with order dependency, which makes the Transformer-based NMT achieve state-of-the-art results for various translation tasks. However, Transformer-based NMT only adds representations of positions sequentially to word vectors in the input sentence and does not explicitly consider reordering information in this sentence. In this paper, we first empirically investigate the relationship between source reordering information and translation performance. The empirical findings show that the source input with the target order learned from the bilingual parallel dataset can substantially improve translation performance. Thus, we propose a novel reordering method to explicitly model this reordering information for the Transformer-based NMT. The empirical results on the WMT14 English-to-German, WAT ASPEC Japanese-to-English, and WMT17 Chinese-to-English translation tasks show the effectiveness of the proposed approach.

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