Better Statistical Machine Translation through Linguistic Treatment of Phrasal Verbs

This article describes a linguistically informed method for integrating phrasal verbs into statistical machine translation (SMT) systems. In a case study involving English to Bulgarian SMT, we show that our method does not only improve translation quality but also outperforms similar methods previously applied to the same task. We attribute this to the fact that, in contrast to previous work on the subject, we employ detailed linguistic information. We found out that features which describe phrasal verbs as idiomatic or compositional contribute most to the better translation quality achieved by our method.

• Publication year

  2014

• Venue

  Conference on Empirical Methods in Natural Language Processing

• Publication date

  2014-10-01

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/v1/D14-1024
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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