Incremental Re-training for Post-editing SMT

Daniel Hardt, Jakob Elming


Abstract
A method is presented for incremental re-training of an SMT system, in which a local phrase table is created and incrementally updated as a file is translated and post-edited. It is shown that translation data from within the same file has higher value than other domain-specific data. In two technical domains, within-file data increases BLEU score by several full points. Furthermore, a strong recency effect is documented; nearby data within the file has greater value than more distant data. It is also shown that the value of translation data is strongly correlated with a metric defined over new occurrences of n-grams. Finally, it is argued that the incremental re-training prototype could serve as the basis for a practical system which could be interactively updated in real time in a post-editing setting. Based on the results here, such an interactive system has the potential to dramatically improve translation quality.
Anthology ID:
2010.amta-papers.21
Volume:
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 31-November 4
Year:
2010
Address:
Denver, Colorado, USA
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
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Pages:
Language:
URL:
https://aclanthology.org/2010.amta-papers.21
DOI:
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Cite (ACL):
Daniel Hardt and Jakob Elming. 2010. Incremental Re-training for Post-editing SMT. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Incremental Re-training for Post-editing SMT (Hardt & Elming, AMTA 2010)
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PDF:
https://aclanthology.org/2010.amta-papers.21.pdf