Probabilistic Model for Example-based Machine Translation

Eiji Aramaki, Sadao Kurohashi, Hideki Kashioka, Naoto Kato


Abstract
Example-based machine translation (EBMT) systems, so far, rely on heuristic measures in retrieving translation examples. Such a heuristic measure costs time to adjust, and might make its algorithm unclear. This paper presents a probabilistic model for EBMT. Under the proposed model, the system searches the translation example combination which has the highest probability. The proposed model clearly formalizes EBMT process. In addition, the model can naturally incorporate the context similarity of translation examples. The experimental results demonstrate that the proposed model has a slightly better translation quality than state-of-the-art EBMT systems.
Anthology ID:
2005.mtsummit-papers.29
Volume:
Proceedings of Machine Translation Summit X: Papers
Month:
September 13-15
Year:
2005
Address:
Phuket, Thailand
Venue:
MTSummit
SIG:
Publisher:
Note:
Pages:
219–226
Language:
URL:
https://aclanthology.org/2005.mtsummit-papers.29
DOI:
Bibkey:
Cite (ACL):
Eiji Aramaki, Sadao Kurohashi, Hideki Kashioka, and Naoto Kato. 2005. Probabilistic Model for Example-based Machine Translation. In Proceedings of Machine Translation Summit X: Papers, pages 219–226, Phuket, Thailand.
Cite (Informal):
Probabilistic Model for Example-based Machine Translation (Aramaki et al., MTSummit 2005)
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PDF:
https://aclanthology.org/2005.mtsummit-papers.29.pdf