Use of Domain-Specific Language Resources in Machine Translation

Sanja Štajner, Andreia Querido, Nuno Rendeiro, João António Rodrigues, António Branco

Abstract
In this paper, we address the problem of Machine Translation (MT) for a specialised domain in a language pair for which only a very small domain-specific parallel corpus is available. We conduct a series of experiments using a purely phrase-based SMT (PBSMT) system and a hybrid MT system (TectoMT), testing three different strategies to overcome the problem of the small amount of in-domain training data. Our results show that adding a small size in-domain bilingual terminology to the small in-domain training corpus leads to the best improvements of a hybrid MT system, while the PBSMT system achieves the best results by adding a combination of in-domain bilingual terminology and a larger out-of-domain corpus. We focus on qualitative human evaluation of the output of two best systems (one for each approach) and perform a systematic in-depth error analysis which revealed advantages of the hybrid MT system over the pure PBSMT system for this specific task.
Anthology ID:
L16-1094
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:
592–598
Language:
URL:
https://aclanthology.org/L16-1094
DOI:
Bibkey:
Cite (ACL):
Sanja Štajner, Andreia Querido, Nuno Rendeiro, João António Rodrigues, and António Branco. 2016. Use of Domain-Specific Language Resources in Machine Translation. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 592–598, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Use of Domain-Specific Language Resources in Machine Translation (Štajner et al., LREC 2016)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/L16-1094.pdf
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