Context-aware Neural Machine Translation with Coreference Information

Takumi Ohtani, Hidetaka Kamigaito, Masaaki Nagata, Manabu Okumura


Abstract
We present neural machine translation models for translating a sentence in a text by using a graph-based encoder which can consider coreference relations provided within the text explicitly. The graph-based encoder can dynamically encode the source text without attending to all tokens in the text. In experiments, our proposed models provide statistically significant improvement to the previous approach of at most 0.9 points in the BLEU score on the OpenSubtitle2018 English-to-Japanese data set. Experimental results also show that the graph-based encoder can handle a longer text well, compared with the previous approach.
Anthology ID:
D19-6505
Volume:
Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
DiscoMT | EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
45–50
Language:
URL:
https://www.aclweb.org/anthology/D19-6505
DOI:
10.18653/v1/D19-6505
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-6505.pdf