Stochastic finite-state models for spoken language machine translation

Authors:
Srinivas Bangalore;Giuseppe Riccardi
Affiliations:
AT&T Labs -- Research, Florham Park, NJ;AT&T Labs -- Research, Florham Park, NJ
Venue:
EmbedMT '00 ANLP-NAACL 2000 Workshop: Embedded Machine Translation Systems
Year:
2000

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Abstract

Stochastic finite-state models are efficiently learnable from data, effective for decoding and are associated with a calculus for composing models which allows for tight integration of constraints from various levels of language processing. In this paper, we present a method for stochastic finite-state machine translation that is trained automatically from pairs of source and target utterances. We use this method to develop models for English-Japanese and Japanese-English translation. We have embedded the Japanese-English translation system in a call routing task of unconstrained speech utterances. We evaluate the efficacy of the translation system in the context of this application.