Conference item
Online segment to segment neural transduction
- Abstract:
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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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Authors
Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Yu, L
Bibliographic Details
- 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:
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648417
Item Description
Terms of use
- Copyright holder:
- Association for Computational Linguistics
- Copyright date:
- 2016
- Notes:
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Copyright © 2016 Association for Computational Linguistics
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- Licence:
- CC Attribution (CC BY)
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