Papers by Yoshinobu Kano
Diverse dialogue generation with context dependent dynamic loss function (2020.coling-main)
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| Challenge: | Dialogue systems using deep learning have achieved generation of fluent response sentences to user utterances, but they tend to produce responses that are not diverse and less context-dependent. |
| Approach: | They propose an Inverse N-gram loss function which incorporates contextual fluency and diversity at the same time by a simple formula. |
| Outcome: | The proposed loss function outperforms baseline models in automatic evaluations such as DIST-N and ROUGE and achieves higher scores on human evaluations of coherence and richness. |
ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation (2021.findings-acl)
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| Challenge: | Recent advances in NLP focus on large annotated training data. |
| Approach: | They propose an unsupervised framework that does not use parallel or pseudo-parallel/back-translated data. |
| Outcome: | The proposed framework does not use parallel or pseudo-parallel/back-translated data. |