Corpus ID: 49669485

Latent Alignment and Variational Attention

@article{Deng2018LatentAA,
  title={Latent Alignment and Variational Attention},
  author={Y. Deng and Yoon Kim and Justin T Chiu and Demi Guo and Alexander M. Rush},
  journal={ArXiv},
  year={2018},
  volume={abs/1807.03756}
}

Y. Deng, Yoon Kim, +2 authors Alexander M. Rush

Published 2018

Computer Science, Mathematics

ArXiv

Neural attention has become central to many state-of-the-art models in natural language processing and related domains. Attention networks are an easy-to-train and effective method for softly simulating alignment; however, the approach does not marginalize over latent alignments in a probabilistic sense. This property makes it difficult to compare attention to other alignment approaches, to compose it with probabilistic models, and to perform posterior inference conditioned on observed data. A… CONTINUE READING

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