Statistical Machine Translation for bilingually low-resource scenarios: a round-tripping approach

Benyamin Ahmadnia, Gholamreza Haffari, Javier Serrano

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

5 Citations (Scopus)


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 languageEnglish
Title of host publication5th International IEEE Congress on Information Science and Technology, IEEE CiSt'18
Subtitle of host publicationMarrakech, Morocco, October 21-27, 2018
EditorsIsmaii Jeiiouii
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781538643853
ISBN (Print)9781538643860
Publication statusPublished - 2018
EventInternational Congress on Information Science and Technology 2018 - Marrakech, Morocco
Duration: 21 Oct 201827 Oct 2018
Conference number: 5th

Publication series

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


ConferenceInternational Congress on Information Science and Technology 2018
Abbreviated titleCiSt 2018
Internet address


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

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