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Online segment to segment neural transduction

Abstract:

We introduce an online neural sequence to sequence model that learns to alternate between encoding and decoding segments of the input as it is read. By independently tracking the encoding and decoding representations our algorithm permits exact polynomial marginalization of the latent segmentation during training, and during decoding beam search is employed to find the best alignment path together with the predicted output sequence. Our model tackles the bottleneck of vanilla encoder-decoders...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Oxford college:
Mansfield College
Role:
Author
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Institution:
University of Oxford
Oxford college:
University College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
Publisher:
Association for Computational Linguistics Publisher's website
Journal:
Empirical Methods on Natural Language Processing Conference 2016 Journal website
Pages:
1307–1316
Host title:
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
Publication date:
2016-11-01
Acceptance date:
2016-07-29
Source identifiers:
648417
Keywords:
Pubs id:
pubs:648417
UUID:
uuid:789d1f0a-4428-45d4-9f27-6e80a74d7e37
Local pid:
pubs:648417
Deposit date:
2016-10-16

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