A comparison of linguistically and statistically enhanced models for speech-to-speech machine translation

Alicia Pérez, Víctor Guijarrubia, Raquel Justo, M. Inés Torres, Francisco Casacuberta


Abstract
The goal of this work is to improve current translation models by taking into account additional knowledge sources such as semantically motivated segmentation or statistical categorization. Specifically, two different approaches are discussed. On the one hand, phrase-based approach, and on the other hand, categorization. For both approaches, both statistical and linguistic alternatives are explored. As for translation framework, finite-state transducers are considered. These are versatile models that can be easily integrated on-the-fly with acoustic models for speech translation purposes. In what the experimental framework concerns, all the models presented were evaluated and compared taking confidence intervals into account.
Anthology ID:
2007.iwslt-1.2
Volume:
Proceedings of the Fourth International Workshop on Spoken Language Translation
Month:
October 15-16
Year:
2007
Address:
Trento, Italy
Venue:
IWSLT
SIG:
SIGSLT
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Pages:
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URL:
https://aclanthology.org/2007.iwslt-1.2
DOI:
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Cite (ACL):
Alicia Pérez, Víctor Guijarrubia, Raquel Justo, M. Inés Torres, and Francisco Casacuberta. 2007. A comparison of linguistically and statistically enhanced models for speech-to-speech machine translation. In Proceedings of the Fourth International Workshop on Spoken Language Translation, Trento, Italy.
Cite (Informal):
A comparison of linguistically and statistically enhanced models for speech-to-speech machine translation (Pérez et al., IWSLT 2007)
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PDF:
https://aclanthology.org/2007.iwslt-1.2.pdf