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

Benyamin Ahmadnia, Gholamreza Haffari, Javier Serrano

Research output: Chapter in BookChapterResearchpeer-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
Title of host publication5th International Congress on Information Science and Technology, CiSt 2018
EditorsMohammed Al Achhab, Mohammed El Mohajir, Ismail Jellouli, Badr Eddine El Mohajir
Pages261-265
Number of pages5
ISBN (Electronic)9781538643853
DOIs
Publication statusPublished - 28 Dec 2018

Publication series

NameColloquium in Information Science and Technology, CIST
Volume2018-October
ISSN (Print)2327-185X
ISSN (Electronic)2327-1884

Keywords

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

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