A Joint Dependency Model of Morphological and Syntactic Structure for Statistical Machine Translation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

When translating between two languages that differ in their degree of morphological synthesis, syntactic structures in one language may be realized as morphological structures in the other, and SMT models need a mechanism to learn such translations. Prior work has used morpheme splitting with flat representations that do not encode the hierarchical structure between morphemes, but this structure is relevant for learning morphosyntactic constraints and selectional preferences. We propose to model syntactic and morphological structure jointly in a dependency translation model, allowing the system to generalize to the level of morphemes. We present a dependency representation of German compounds and particle verbs that results in improvements in translation quality of 1.4–1.8 BLEU in the WMT English–German translation task.
Original languageEnglish
Title of host publicationProceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Place of PublicationLisbon, Portugal
PublisherAssociation for Computational Linguistics
Pages2081-2087
Number of pages7
DOIs
Publication statusPublished - 1 Sep 2015
Event2015 Conference on Empirical Methods in Natural Language Processing - Lisbon, Portugal
Duration: 17 Sep 201521 Sep 2015
http://www.emnlp2015.org/

Conference

Conference2015 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2015
Country/TerritoryPortugal
CityLisbon
Period17/09/1521/09/15
Internet address

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