Combination of Machine Translation Systems via Hypothesis Selection from Combined N-Best Lists

Almut Silja Hildebrand, Stephan Vogel


Abstract
Different approaches in machine translation achieve similar translation quality with a variety of translations in the output. Recently it has been shown, that it is possible to leverage the individual strengths of various systems and improve the overall translation quality by combining translation outputs. In this paper we present a method of hypothesis selection which is relatively simple compared to system combination methods which construct a synthesis of the input hypotheses. Our method uses information from n-best lists from several MT systems and features on the sentence level which are independent from the MT systems involved to improve the translation quality.
Anthology ID:
2008.amta-srw.3
Volume:
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Student Research Workshop
Month:
October 21-25
Year:
2008
Address:
Waikiki, USA
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
254–261
Language:
URL:
https://aclanthology.org/2008.amta-srw.3
DOI:
Bibkey:
Cite (ACL):
Almut Silja Hildebrand and Stephan Vogel. 2008. Combination of Machine Translation Systems via Hypothesis Selection from Combined N-Best Lists. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Student Research Workshop, pages 254–261, Waikiki, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Combination of Machine Translation Systems via Hypothesis Selection from Combined N-Best Lists (Hildebrand & Vogel, AMTA 2008)
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PDF:
https://preview.aclanthology.org/nodalida-main-page/2008.amta-srw.3.pdf