Improving MT coherence through text- level processing of input texts: the COMTIS project. Session 6 - Translation and Natural Language Processing

Fiche du document

Auteurs
Date

3 mars 2011

Discipline
Type de document
Périmètre
Langue
Identifiants
Source

HAL SHS (Sciences de l’Homme et de la Société)

Collection

Archives ouvertes

Organisation

Centre pour la communication scientifique directe

Licence

info:eu-repo/semantics/OpenAccess

Mots-clés En

Tralogy I

Sujets proches En

Language (New words, slang, etc.) Natural science Sciences Science of science

Citer ce document

Andrei Popescu-Belis et al., « Improving MT coherence through text- level processing of input texts: the COMTIS project. Session 6 - Translation and Natural Language Processing », HAL SHS (Sciences de l’Homme et de la Société), ID : 10670/1.a15728...

Métriques

Partage / Export

Résumé En

This paper presents an ongoing research project, started in March 2010 and sponsored by the Swiss National Science Foundation, which aims at improving machine translation output in terms of textual coherence. Coherence in text is mainly due to inter-sentential dependencies. Statistical Machine Translation (SMT) systems, currently sentence-based, often fail to translate these dependencies correctly. Within the COMTIS project, state-of-the-art linguistics research and Natural Language Processing (NLP) techniques are combined to identify and to label inter-sentential dependencies that can be learned by SMT system in the training phase.

document thumbnail

Par les mêmes auteurs

Sur les mêmes sujets

Sur les mêmes disciplines