Abstract
Currently under review for EMNLP 2017 The phrase-based Statistical Machine Translation (SMT) approach deals with sentences in isolation, making it difficult to consider discourse context in translation. This poses a challenge for ambiguous words that need discourse knowledge to be correctly translated. We propose a method that benefits from the semantic similarity in lexical chains to improve SMT output by integrating it in a document-level decoder. We focus on word embeddings to deal with the lexical chains, contrary to the traditional approach that uses lexical resources. Experimental results on German-to-English show that our method produces correct translations in up to 88% of the changes, improving the translation in 36%-48% of them over the baseline.- Anthology ID:
- W17-4813
- Volume:
- Proceedings of the Third Workshop on Discourse in Machine Translation
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Bonnie Webber, Andrei Popescu-Belis, Jörg Tiedemann
- Venue:
- DiscoMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 99–109
- Language:
- URL:
- https://aclanthology.org/W17-4813
- DOI:
- 10.18653/v1/W17-4813
- Cite (ACL):
- Laura Mascarell. 2017. Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation. In Proceedings of the Third Workshop on Discourse in Machine Translation, pages 99–109, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation (Mascarell, DiscoMT 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W17-4813.pdf
- Data
- Europarl