Abstract
We propose a new method for extracting pseudo-parallel sentences from a pair of large monolingual corpora, without relying on any document-level information. Our method first exploits word embeddings in order to efficiently evaluate trillions of candidate sentence pairs and then a classifier to find the most reliable ones. We report significant improvements in domain adaptation for statistical machine translation when using a translation model trained on the sentence pairs extracted from in-domain monolingual corpora.- Anthology ID:
- P17-2062
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 392–398
- Language:
- URL:
- https://aclanthology.org/P17-2062
- DOI:
- 10.18653/v1/P17-2062
- Cite (ACL):
- Benjamin Marie and Atsushi Fujita. 2017. Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 392–398, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings (Marie & Fujita, ACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/P17-2062.pdf