Papers by Ryota Takahashi
Deep Reinforcement Learning with Hierarchical Action Exploration for Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing approaches to improve dialogues with random sampling are inefficient due to the large number of eligible responses with high action values. |
| Approach: | They propose a dual-granularity Q-function that extracts actions based on a grained hierarchy . they use offline RL and learn from multiple reward functions designed to capture emotional nuances in human interactions. |
| Outcome: | The proposed approach outperforms baselines across automatic metrics and human evaluations. |
A Personalized Dialogue Generator with Implicit User Persona Detection (2022.coling-1)
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| Challenge: | Existing models for personalized dialogue generation tend to be self-centered, with little care for the user in the dialogue. |
| Approach: | They propose a personalized dialogue generator by detecting an implicit user persona and using conditional variational inference to model the user's potential persona with no external knowledge. |
| Outcome: | The proposed model improves both automatic metrics and human evaluations by focusing on the user's persona and posterior-discriminated regularization. |