Using a Graph-based Coherence Model in Document-Level Machine Translation

Leo Born, Mohsen Mesgar, Michael Strube


Abstract
Although coherence is an important aspect of any text generation system, it has received little attention in the context of machine translation (MT) so far. We hypothesize that the quality of document-level translation can be improved if MT models take into account the semantic relations among sentences during translation. We integrate the graph-based coherence model proposed by Mesgar and Strube, (2016) with Docent (Hardmeier et al., 2012, Hardmeier, 2014) a document-level machine translation system. The application of this graph-based coherence modeling approach is novel in the context of machine translation. We evaluate the coherence model and its effects on the quality of the machine translation. The result of our experiments shows that our coherence model slightly improves the quality of translation in terms of the average Meteor score.
Anthology ID:
W17-4803
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:
26–35
Language:
URL:
https://www.aclweb.org/anthology/W17-4803
DOI:
10.18653/v1/W17-4803
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4803.pdf