Document-Wide Decoding for Phrase-Based Statistical Machine Translation
2012 (English)In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics, 2012, 1179-1190 p.Conference paper (Refereed)
Independence between sentences is an assumption deeply entrenched in the models and algorithms used for statistical machine translation (SMT), particularly in the popular dynamic programming beam search decoding algorithm. This restriction is an obstacle to research on more sophisticated discourse-level models for SMT. We propose a stochastic local search decoding method for phrase-based SMT, which permits free document-wide dependencies in the models. We explore the stability and the search parameters of this method and demonstrate that it can be successfully used to optimise a document-level semantic language model.
Place, publisher, year, edition, pages
Association for Computational Linguistics, 2012. 1179-1190 p.
Language Technology (Computational Linguistics)
Research subject Computational Linguistics
IdentifiersURN: urn:nbn:se:uu:diva-189738OAI: oai:DiVA.org:uu-189738DiVA: diva2:582223
Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning; 12-14 July 2012; Jeju Island, Korea