Document-aligned Japanese-English Conversation Parallel Corpus

• URL: http://arxiv.org/abs/2012.06143v1
• Date: Fri, 11 Dec 2020 06:03:33 GMT
• Title: Document-aligned Japanese-English Conversation Parallel Corpus
• Authors: Mat\=iss Rikters, Ryokan Ri, Tong Li, Toshiaki Nakazawa
• Abstract summary: Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT. We present a document-aligned Japanese-English conversation corpus, including balanced, high-quality business conversation data for tuning and testing. We train MT models using our corpus to demonstrate how using context leads to improvements.
• Score: 4.793904440030568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation. To address the first issue, we present a document-aligned Japanese-English conversation corpus, including balanced, high-quality business conversation data for tuning and testing. As for the second issue, we manually identify the main areas where SL MT fails to produce adequate translations in lack of context. We then create an evaluation set where these phenomena are annotated to alleviate automatic evaluation of DL systems. We train MT models using our corpus to demonstrate how using context leads to improvements.

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