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        <title>Using a Graph-based Coherence Model in Document-Level Machine Translation</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Using a Graph-based Coherence Model in Document-Level Machine Translation
%A Born, Leo
%A Mesgar, Mohsen
%A Strube, Michael
%Y Webber, Bonnie
%Y Popescu-Belis, Andrei
%Y Tiedemann, Jörg
%S Proceedings of the Third Workshop on Discourse in Machine Translation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F born-etal-2017-using
%X 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.
%R 10.18653/v1/W17-4803
%U https://aclanthology.org/W17-4803
%U https://doi.org/10.18653/v1/W17-4803
%P 26-35

Markdown (Informal)

[Using a Graph-based Coherence Model in Document-Level Machine Translation](https://aclanthology.org/W17-4803) (Born et al., DiscoMT 2017)

• Using a Graph-based Coherence Model in Document-Level Machine Translation (Born et al., DiscoMT 2017)

ACL

• Leo Born, Mohsen Mesgar, and Michael Strube. 2017. Using a Graph-based Coherence Model in Document-Level Machine Translation. In Proceedings of the Third Workshop on Discourse in Machine Translation, pages 26–35, Copenhagen, Denmark. Association for Computational Linguistics.