Document-level Neural Machine Translation with Document Embeddings

• URL: http://arxiv.org/abs/2009.08775v1
• Date: Wed, 16 Sep 2020 19:43:29 GMT
• Title: Document-level Neural Machine Translation with Document Embeddings
• Authors: Shu Jiang, Hai Zhao, Zuchao Li, Bao-Liang Lu
• Abstract summary: This work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings. The proposed document-aware NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end.
• Score: 82.4684444847092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Standard neural machine translation (NMT) is on the assumption of document-level context independent. Most existing document-level NMT methods are satisfied with a smattering sense of brief document-level information, while this work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings, which is capable of sufficiently modeling deeper and richer document-level context. The proposed document-aware NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end. Experiments show that the proposed method significantly improves the translation performance over strong baselines and other related studies.

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