Improving Pivot Translation by Remembering the Pivot

Pivot translation allows for translation of language pairs with little or no parallel data by introducing a third language for which data exists. In particular, the triangulation method, which translates by combining source-pivot and pivot-target translation models into a source-target model, is known for its high translation accuracy. However, in the conventional triangulation method, information of pivot phrases is forgotten and not used in the translation process. In this paper, we propose a novel approach torememberthe pivot phrases in the triangulation stage, and use a pivot language model as an additional information source at translation time. Experimental results on the Europarl corpus showed gains of 0.4-1.2 BLEU points in all tested combinations of languages 1 .

• Publication year

  2015

• Venue

  Annual Meeting of the Association for Computational Linguistics

• Publication date

  2015-07-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3115/v1/P15-2094
• 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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