Papers by Hiroaki Sugiyama

5 papers
Time-Considerable Dialogue Models via Reranking by Time Dependency (2023.findings-emnlp)

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Challenge: Existing dialogue models do not consider time information that humans are constantly aware of.
Approach: They propose to categorize responses by their naturalness at different times and introduce a new metric to classify responses into categories.
Outcome: The proposed model categorizes responses by their naturalness at different times and evaluates them subjectively.
Let’s Put Ourselves in Sally’s Shoes: Shoes-of-Others Prefilling Improves Theory of Mind in Large Language Models (2026.findings-eacl)

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Challenge: Existing methods for Theory of Mind (ToM) are specialized for inferring beliefs from contexts involving changes in the world state.
Approach: They propose a method which makes fewer assumptions about contexts and is applicable to broader scenarios.
Outcome: The proposed method makes fewer assumptions about contexts and is applicable to broader scenarios.
Collection of Multimodal Dialog Data and Analysis of the Result of Annotation of Users’ Interest Level (L18-1)

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Challenge: a group of researchers is building a corpus for evaluating elements of multimodal dialogue systems.
Approach: They propose to build a corpus for evaluating elements of the multimodal dialogue system . they use the Wizard of Oz method to record chat dialogue data between a human and a virtual agent .
Outcome: The proposed method annotates chat dialogue data between a human and a virtual agent and measures their interest level in the data.
Comparison of the Intimacy Process between Real and Acting-based Long-term Text Chats (2024.lrec-main)

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Challenge: Recent open-domain chatbots can generate natural responses over multiple turns using large-scale language models, but they do not address the speaker intimacy process and thus cannot sustain natural dialogue over multiple days and weeks.
Approach: They propose to train systems with multi-session chat data to simulate relationship-building between speakers.
Outcome: The proposed multi-session chat data can simulate relationship-building between speakers but has not been tested in Japanese.
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.

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