Papers by Atsumoto Ohashi
On the True Distribution Approximation of Minimum Bayes-Risk Decoding (2024.naacl-short)
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| Challenge: | Minimum Bayes-risk (MBR) decoding has recently gained renewed attention in text generation. |
| Approach: | They propose to use anomaly detection to measure the degree of approximation by sampling texts from a model and selecting the text with the highest similarity to the others. |
| Outcome: | The proposed method shows that previous hypotheses about samples do not correlate well with the variation, but the results support the core assumption of MBR decoding. |
Enhancing Task-oriented Dialogue Systems with Generative Post-processing Networks (2023.emnlp-main)
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| Challenge: | Recent work proposes a method to optimize pipelined dialogue systems by fine-tuning modules directly. |
| Approach: | They propose a new post-processing component for natural language generation (NLG) they use dialogue act contribution to evaluate contribution of GenPPN-generated utterances . |
| Outcome: | The proposed method improves the performance of task-oriented dialogue systems by modifying arbitrary modules including non-differentiable ones. |
JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset (2024.lrec-main)
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| Challenge: | Existing datasets for task-oriented dialogue systems in English are limited compared to Japanese. |
| Approach: | They evaluated the dialogue state tracking and response generation capabilities of Japanese language datasets using multi-domain task-oriented dialogues. |
| Outcome: | The proposed dataset provides a benchmark that is on par with MultiWOZ2.2 and the latest large language model (LLM)-based methods. |
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning (2022.coling-1)
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| Challenge: | In task-oriented dialogue systems, the role of the natural language generation component is to convert a system's intentions, called dialogue acts (DAs), into natural language utterances and to convey DAs accurately to users. |
| Approach: | They propose a method for Adaptive Natural language generation for Task-Oriented dialogue via Reinforcement learning that incorporates a natural language understanding module into the objective function of RL. |
| Outcome: | The proposed method generates adaptive utterances against speech recognition errors and the different vocabulary levels of users in a multi-world task-oriented dialogue system. |