Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88461
Title: Discourse structure in machine translation evaluation
Authors: Joty, Shafiq
Guzmán, Francisco
Màrquez, Lluís
Nakov, Preslav
Keywords: Computer Aided Language Translation
Machine Translation Evaluation
DRNTU::Engineering::Computer science and engineering
Issue Date: 2017
Source: Joty, 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_00298
Series/Report no.: Computational Linguistics
Abstract: In 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.
URI: https://hdl.handle.net/10356/88461
http://hdl.handle.net/10220/46925
ISSN: 0891-2017
DOI: http://dx.doi.org/10.1162/COLI_a_00298
Rights: © 2017 Association for Computational Linguistics. Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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