On Integrating Discourse in Machine Translation

Karin Sim Smith


Abstract
As the quality of Machine Translation (MT) improves, research on improving discourse in automatic translations becomes more viable. This has resulted in an increase in the amount of work on discourse in MT. However many of the existing models and metrics have yet to integrate these insights. Part of this is due to the evaluation methodology, based as it is largely on matching to a single reference. At a time when MT is increasingly being used in a pipeline for other tasks, the semantic element of the translation process needs to be properly integrated into the task. Moreover, in order to take MT to another level, it will need to judge output not based on a single reference translation, but based on notions of fluency and of adequacy – ideally with reference to the source text.
Anthology ID:
W17-4814
Volume:
Proceedings of the Third Workshop on Discourse in Machine Translation
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
DiscoMT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–121
Language:
URL:
https://www.aclweb.org/anthology/W17-4814
DOI:
10.18653/v1/W17-4814
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4814.pdf