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        <title>Learning to Discover, Ground and Use Words with Segmental Neural Language Models</title>
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    <abstract>We propose a segmental neural language model that combines the generalization power of neural networks with the ability to discover word-like units that are latent in unsegmented character sequences. In contrast to previous segmentation models that treat word segmentation as an isolated task, our model unifies word discovery, learning how words fit together to form sentences, and, by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. Experiments show that the unconditional model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and that modeling language conditional on visual context improves performance on both.</abstract>
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%0 Conference Proceedings
%T Learning to Discover, Ground and Use Words with Segmental Neural Language Models
%A Kawakami, Kazuya
%A Dyer, Chris
%A Blunsom, Phil
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F kawakami-etal-2019-learning
%X We propose a segmental neural language model that combines the generalization power of neural networks with the ability to discover word-like units that are latent in unsegmented character sequences. In contrast to previous segmentation models that treat word segmentation as an isolated task, our model unifies word discovery, learning how words fit together to form sentences, and, by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. Experiments show that the unconditional model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and that modeling language conditional on visual context improves performance on both.
%R 10.18653/v1/P19-1645
%U https://aclanthology.org/P19-1645
%U https://doi.org/10.18653/v1/P19-1645
%P 6429-6441

Markdown (Informal)

[Learning to Discover, Ground and Use Words with Segmental Neural Language Models](https://aclanthology.org/P19-1645) (Kawakami et al., ACL 2019)

• Learning to Discover, Ground and Use Words with Segmental Neural Language Models (Kawakami et al., ACL 2019)

ACL

• Kazuya Kawakami, Chris Dyer, and Phil Blunsom. 2019. Learning to Discover, Ground and Use Words with Segmental Neural Language Models. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6429–6441, Florence, Italy. Association for Computational Linguistics.