Domain Adaptation for Statistical Machine Translation
type: Dataset
description: These last years have seen the development of statistical approaches for machine translation. Nevertheless, the intrinsic variations of the natural language act upon the quality of statistical models. Studies have shown that in-domain corpora containwords that can occur in out-of-domain corpora (common words), but also contain domain specific words. This particularity can be handled by terminological resources like bilingual lexicons. However, if the vocabulary differs between out and in-domain data, the syntactic and semantic content may also vary. In our work, we consider the task of domain adaptation for statistical machine translation through two majoraxes : bilingual lexicon acquisition and post-edition of machine translation outputs.We evaluate our approaches on the medical domain. The quality of automatic translations in the medical domain are improved and the results are compared to other works in this field. Oracle evaluations tend to show that further gains are still possible
identifier: NNT: 2011AVIG0186
issued: 2026-05-09T03:44:23.499539
language: fr
modified: 2026-05-09T03:44:23.499543
contact point: Organization
  fn: CCSD
keyword: bilingual-lexicon
keyword: domaine-specialise
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: lexique-bilingue
keyword: post-edition
keyword: specific-domain
keyword: statistical-machine-translation
keyword: terminologie
keyword: terminology
keyword: theses
keyword: traduction-automatique-statistique
landing page: https://theses.hal.science/tel-00879945

Domain Adaptation for Statistical Machine Translation
type: Distribution
format: HTML
issued: 2026-05-09T03:44:23.502187
modified: 2026-05-09T03:44:23.483777
access URL: https://theses.hal.science/tel-00879945

test_moissonnage_selune
type: Agent