Kazuya Kawakami


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Learning Robust and Multilingual Speech Representations
Kazuya Kawakami | Luyu Wang | Chris Dyer | Phil Blunsom | Aaron van den Oord
Findings of the Association for Computational Linguistics: EMNLP 2020

Unsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. However, most research has been focused on evaluating the representations in terms of their ability to improve the performance of speech recognition systems on read English (e.g. Wall Street Journal and LibriSpeech). This evaluation methodology overlooks two important desiderata that speech representations should have: robustness to domain shifts and transferability to other languages. In this paper we learn representations from up to 8000 hours of diverse and noisy speech data and evaluate the representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. We find that our representations confer significant robustness advantages to the resulting recognition systems: we see significant improvements in out-of-domain transfer relative to baseline feature sets and the features likewise provide improvements in 25 phonetically diverse languages.


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Learning to Discover, Ground and Use Words with Segmental Neural Language Models
Kazuya Kawakami | Chris Dyer | Phil Blunsom
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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.


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Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling
Kazuya Kawakami | Chris Dyer | Phil Blunsom
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Fixed-vocabulary language models fail to account for one of the most characteristic statistical facts of natural language: the frequent creation and reuse of new word types. Although character-level language models offer a partial solution in that they can create word types not attested in the training corpus, they do not capture the “bursty” distribution of such words. In this paper, we augment a hierarchical LSTM language model that generates sequences of word tokens character by character with a caching mechanism that learns to reuse previously generated words. To validate our model we construct a new open-vocabulary language modeling corpus (the Multilingual Wikipedia Corpus; MWC) from comparable Wikipedia articles in 7 typologically diverse languages and demonstrate the effectiveness of our model across this range of languages.


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Character Sequence Models for Colorful Words
Kazuya Kawakami | Chris Dyer | Bryan Routledge | Noah A. Smith
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Neural Architectures for Named Entity Recognition
Guillaume Lample | Miguel Ballesteros | Sandeep Subramanian | Kazuya Kawakami | Chris Dyer
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies