Document-aligned Japanese-English Conversation Parallel Corpus
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.
Cite
@article{arxiv.2012.06143,
title = {Document-aligned Japanese-English Conversation Parallel Corpus},
author = {Matīss Rikters and Ryokan Ri and Tong Li and Toshiaki Nakazawa},
journal= {arXiv preprint arXiv:2012.06143},
year = {2020}
}
Comments
Published in proceedings of the Fifth Conference on Machine Translation, 2020