Using BabelNet to Improve OOV Coverage in SMT

Jinhua Du, Andy Way, Andrzej Zydron


Abstract
Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies different strategies of using BabelNet to alleviate the negative impact brought about by OOVs. BabelNet is a multilingual encyclopedic dictionary and a semantic network, which not only includes lexicographic and encyclopedic terms, but connects concepts and named entities in a very large network of semantic relations. By taking advantage of the knowledge in BabelNet, three different methods ― using direct training data, domain-adaptation techniques and the BabelNet API ― are proposed in this paper to obtain translations for OOVs to improve system performance. Experimental results on English―Polish and English―Chinese language pairs show that domain adaptation can better utilize BabelNet knowledge and performs better than other methods. The results also demonstrate that BabelNet is a really useful tool for improving translation performance of SMT systems.
Anthology ID:
L16-1002
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
9–15
Language:
URL:
https://aclanthology.org/L16-1002
DOI:
Bibkey:
Cite (ACL):
Jinhua Du, Andy Way, and Andrzej Zydron. 2016. Using BabelNet to Improve OOV Coverage in SMT. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 9–15, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Using BabelNet to Improve OOV Coverage in SMT (Du et al., LREC 2016)
Copy Citation:
PDF:
https://aclanthology.org/L16-1002.pdf