Using a mixture of N-best lists from multiple MT systems in rank-sum-based confidence measure for MT outputs

Authors:
Yasuhiro Akiba;Eiichiro Sumita;Hiromi Nakaiwa;Seiichi Yamamoto;Hiroshi G. Okuno
Affiliations:
ATR Spoken Language Translation Research Laboratories, Kyoto, Japan and Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, Japan;ATR Spoken Language Translation Research Laboratories, Kyoto, Japan;ATR Spoken Language Translation Research Laboratories, Kyoto, Japan;ATR Spoken Language Translation Research Laboratories, Kyoto, Japan;Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, Japan
Venue:
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Year:
2004

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Abstract

This paper addressees the problem of eliminating unsatisfactory outputs from machine translation (MT) systems. The authors intend to eliminate unsatisfactory MT outputs by using confidence measures. Confidence measures for MT outputs include the rank-sum-based confidence measure (RSCM) for statistical machine translation (SMT) systems. RSCM can be applied to non-SMT systems but does not always work well on them. This paper proposes an alternative RSCM that adopts a mixture of the N-best lists from multiple MT systems instead of a single-system's N-best list in the existing RSCM. In most cases, the proposed RSCM proved to work better than the existing RSCM on two non-SMT systems and to work as well as the existing RSCM on an SMT system.