publication . Conference object . Contribution for newspaper or weekly magazine . 2015

A joint dependency model of morphological and syntactic structure for statistical machine translation

Rico Sennrich; Barry Haddow;
Open Access
  • Published: 01 Jan 2015
When translating between two languages that differ in their degree of morphological synthesis, syntactic structures in one language may be realized as morphological structures in the other, and SMT models need a mechanism to learn such translations. Prior work has used morpheme splitting with flat representations that do not encode the hierarchical structure between morphemes, but this structure is relevant for learning morphosyntactic constraints and selectional preferences. We propose to model syntactic and morphological structure jointly in a dependency translation model, allowing the system to generalize to the level of morphemes. We present a dependency rep...
free text keywords: Machine translation, computer.software_genre, computer, Natural language processing, Speech recognition, Rule-based machine translation, German, language.human_language, language, Computer science, Syntactic structure, Morpheme, ENCODE, Syntax, Artificial intelligence, business.industry, business
Funded by
EC| QT21
QT21: Quality Translation 21
  • Funder: European Commission (EC)
  • Project Code: 645452
  • Funding stream: H2020 | RIA
Validated by funder
EC| HimL
Health in my Language
  • Funder: European Commission (EC)
  • Project Code: 644402
  • Funding stream: H2020 | IA
SNSF| Smarter Model Learning in Syntax-based Statistical Machine Translation
  • Funder: Swiss National Science Foundation (SNSF)
  • Project Code: P2ZHP1_148717
  • Funding stream: Careers;Fellowships | Early Postdoc.Mobility
Translation for Massive Open Online Courses
  • Funder: European Commission (EC)
  • Project Code: 644333
  • Funding stream: H2020 | IA
Digital Humanities and Cultural Heritage
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