Du, JinhuaORCID: 0000-0002-3267-4881, Way, AndyORCID: 0000-0001-5736-5930 and Zydron, Andrzej
(2016)
Using BabelNet to improve OOV coverage in SMT.
In: 2016 International Conference on Language Resources and Evaluation, 23-28 May 2016, Portorož, Slovenia.
ISBN 978-2-9517408-9-1
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.
Calzolari, Nicoletta and Choukri, Khalid, (eds.)
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016).
.
European Language Resources Association. ISBN 978-2-9517408-9-1
This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:
Science Foundation Ireland through the ADAPT Centre (Grant 13/RC/2106) (www.adaptcentre.ie) at Dublin City University and Trinity College Dublin, Grant 610879 for the Falcon project funded by the European Commission
ID Code:
23224
Deposited On:
01 May 2019 15:32 by
Thomas Murtagh
. Last Modified 01 May 2019 15:32