Papers by Kazuya Kawakami
Learning Robust and Multilingual Speech Representations (2020.findings-emnlp)
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| Challenge: | Unsupervised speech representation learning has shown success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. |
| Approach: | They evaluate unsupervised speech representation learning representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. |
| Outcome: | The proposed representations improve the recognition performance in 25 phonetically diverse languages and are robust to domain shifts. |
Learning to Discover, Ground and Use Words with Segmental Neural Language Models (P19-1)
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| Challenge: | Existing models of word learning do not account for the long-range dependencies manifest in language and that are easily captured by recurrent neural networks. |
| Approach: | They propose a segmental neural language model that 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. |
| Outcome: | The proposed model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and improves on both. |