Papers by Shiki Sato
Proactive User Information Acquisition via Chats on User-Favored Topics (2025.findings-emnlp)
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| Challenge: | PIA tasks require a system to acquire user information without making the user feel abrupt while engaging in a chat on a predefined topic. |
| Approach: | They propose a task to acquire user's answers to predefined questions without making the user feel abrupt while engaging in a chat on a predefined topic. |
| Outcome: | The proposed system outperforms LLMs prompted with task instructions in a dataset of 650 PIA chats and shows that it is reasonably accurate. |
User Willingness-aware Sales Talk Dataset (2025.coling-main)
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| Challenge: | Despite the importance of user willingness, to the best of our knowledge, no previous study has addressed the development of automated sales talk dialogue systems that explicitly consider user willingness. |
| Approach: | They developed a user willingness–aware sales talk collection by leveraging the ecological validity concept to elicit natural user willingness. |
| Outcome: | The proposed system elicited user willingness at the utterance level from multiple perspectives and was able to improve the user's intent to purchase. |
Target-Guided Open-Domain Conversation Planning (2022.coling-1)
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| Challenge: | Existing studies on goal-oriented conversational tasks lack planning . prior studies on this topic have focused on generating proactive behavior in agents . |
| Approach: | They propose a task to evaluate whether neural conversational agents have goal-oriented conversation planning abilities. |
| Outcome: | The proposed task evaluates whether neural conversational agents have goal-oriented conversation planning abilities. |
Evaluating Dialogue Generation Systems via Response Selection (2020.acl-main)
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| Challenge: | Existing automatic evaluation metrics for open-domain dialogue systems correlate poorly with human evaluation. |
| Approach: | They propose to construct response selection test sets with well-chosen false candidates to evaluate response generation systems via response selection. |
| Outcome: | The proposed method correlates with human evaluation better than widely used metrics such as BLEU. |
A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems (2024.findings-acl)
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| Challenge: | Recent large-scale neural response generation models (RGMs) have made significant progress but still struggle to generate semantically appropriate responses. |
| Approach: | They build a large dataset of model-generated contradictions for the first time and analyze the results to gain valuable insights into their characteristics. |
| Outcome: | The proposed dataset significantly improves the performance of data-driven contradiction suppression methods. |
Bipartite-play Dialogue Collection for Practical Automatic Evaluation of Dialogue Systems (2022.aacl-srw)
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| Challenge: | Existing methods for dialogue system evaluation are inefficient and time-consuming. |
| Approach: | They propose a dialogue collection method for automating dialogue system evaluation using bipartite-play method . authors propose constructing a better automatic evaluation method which is reproducible and low cost . |
| Outcome: | The proposed method correlates strongly with human subjectivity and human evaluation. |