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A Discriminative Latent Variable Model for Statistical Machine Translation.

Abstract:

Large-scale discriminative machine translation promises to further the state-of-the-art, but has failed to deliver convincing gains over current heuristic frequency count systems.We argue that a principle reason for this failure is not dealing with multiple, equivalent translations. We present a translation model which models derivations as a latent variable, in both training and decoding, and is fully discriminative and globally optimised. Results show that accounting for multiple derivation...

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author

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Role:
Editor
Role:
Editor
Role:
Editor
Role:
Editor
Role:
Editor
Publisher:
Association for Computer Linguistics Publisher's website
Pages:
200-208
Host title:
ACL
Publication date:
2008-01-01
Source identifiers:
328115
ISBN:
9781932432046
Pubs id:
pubs:328115
UUID:
uuid:4a44c3a3-e958-4625-8f6f-d49e6b2c4d82
Local pid:
pubs:328115
Deposit date:
2012-12-19

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