Papers by Takayuki Yamamoto

2 papers
Preference Estimation via Opponent Modeling in Multi-Agent Negotiation (2026.findings-acl)

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Challenge: Existing numerical-only approaches fail to capture qualitative information embedded in natural language interactions, resulting in unstable and incomplete preference estimation.
Approach: They propose a preference estimation method that integrates natural language information into a Bayesian opponent modeling framework.
Outcome: The proposed method improves agreement rate and preference estimation accuracy by integrating probabilistic reasoning with natural language understanding.
Relation Prediction for Unseen-Entities Using Entity-Word Graphs (D19-53)

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Challenge: Knowledge graphs (KGs) are incomplete and miss some information.
Approach: They propose to learn entity representations via a graph structure that uses Seen-entities, Unseen-Entities and words as nodes created from the descriptions of all entities.
Outcome: The proposed method improves relation prediction for the entity pairs containing Unseen-entities.

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