Assessing Divergence Measures for Automated Document Routing in an Adaptive MT System

Claire Jaja, Douglas Briesch, Jamal Laoudi, Clare Voss


Abstract
Custom machine translation (MT) engines systematically outperform general-domain MT engines when translating within the relevant custom domain. This paper investigates the use of the Jensen-Shannon divergence measure for automatically routing new documents within a translation system with multiple MT engines to the appropriate custom MT engine in order to obtain the best translation. Three distinct domains are compared, and the impact of the language, size, and preprocessing of the documents on the Jensen-Shannon score is addressed. Six test datasets are then compared to the three known-domain corpora to predict which of the three custom MT engines they would be routed to at runtime given their Jensen-Shannon scores. The results are promising for incorporating this divergence measure into a translation workflow.
Anthology ID:
L12-1502
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3963–3970
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/843_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Claire Jaja, Douglas Briesch, Jamal Laoudi, and Clare Voss. 2012. Assessing Divergence Measures for Automated Document Routing in an Adaptive MT System. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 3963–3970, Istanbul, Turkey. European Language Resources Association (ELRA).
Cite (Informal):
Assessing Divergence Measures for Automated Document Routing in an Adaptive MT System (Jaja et al., LREC 2012)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/843_Paper.pdf