Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings

Benjamin Marie, Atsushi Fujita


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
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
392–398
Language:
URL:
https://www.aclweb.org/anthology/P17-2062
DOI:
10.18653/v1/P17-2062
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-2062.pdf