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dc.contributor.authorJoty, Shafiqen
dc.contributor.authorGuzmán, Franciscoen
dc.contributor.authorMàrquez, Lluísen
dc.contributor.authorNakov, Preslaven
dc.identifier.citationJoty, S., Guzmán, F., Màrquez, L., & Nakov, P. (2017). Discourse structure in machine translation evaluation. Computational Linguistics, 43(4), 683-722. doi:10.1162/COLI_a_00298en
dc.description.abstractIn this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding correlation with human judgments both at the segment level and at the system level. This suggests that discourse information is complementary to the information used by many of the existing evaluation metrics, and thus it could be taken into account when developing richer evaluation metrics, such as the WMT-14 winning combined metric DiscoTKparty. We also provide a detailed analysis of the relevance of various discourse elements and relations from the RST parse trees for machine translation evaluation. In particular, we show that (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference RST tree is positively correlated with translation quality.en
dc.format.extent40 p.en
dc.relation.ispartofseriesComputational Linguisticsen
dc.rights© 2017 Association for Computational Linguistics. Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licenseen
dc.subjectComputer Aided Language Translationen
dc.subjectMachine Translation Evaluationen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleDiscourse structure in machine translation evaluationen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.description.versionPublished versionen
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