Statistical Machine Translation for Bilingually Low-Resource Scenarios: A Round-Tripping Approach

Benyamin Ahmadnia, Gholamreza Haffari, Javier Serrano

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

In this paper we apply the round-tripping algorithm to Statistical Machine Translation (SMT) for making effective use of monolingual data to tackle the training data scarcity. In this approach, the outbound-trip (forward) and inbound-trip (backward) translation tasks make a closed loop, and produce informative feedback to train the translation models. Based on this produced feedback we iteratively update the forward and backward translation models. The experimental results show that translation quality is improved for Persian\leftrightarrow Spanish translation task.

Original languageAmerican English
Pages (from-to)261-265
Number of pages5
JournalColloquium in Information Science and Technology, CIST
DOIs
Publication statusPublished - 28 Dec 2018

Keywords

  • low-resource languages
  • natural language processing
  • round-tripping algorithm
  • statistical machine translation

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