Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings

Vishrav Chaudhary, Yuqing Tang, Francisco Guzmán, Holger Schwenk, Philipp Koehn


Abstract
In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios.
Anthology ID:
W19-5435
Volume:
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
Month:
August
Year:
2019
Address:
Florence, Italy
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
261–266
Language:
URL:
https://aclanthology.org/W19-5435
DOI:
10.18653/v1/W19-5435
Bibkey:
Cite (ACL):
Vishrav Chaudhary, Yuqing Tang, Francisco Guzmán, Holger Schwenk, and Philipp Koehn. 2019. Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 261–266, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings (Chaudhary et al., WMT 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/W19-5435.pdf