Michimasa Inaba


2023

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AIWolfDial 2023: Summary of Natural Language Division of 5th International AIWolf Contest
Yoshinobu Kano | Neo Watanabe | Kaito Kagaminuma | Claus Aranha | Jaewon Lee | Benedek Hauer | Hisaichi Shibata | Soichiro Miki | Yuta Nakamura | Takuya Okubo | Soga Shigemura | Rei Ito | Kazuki Takashima | Tomoki Fukuda | Masahiro Wakutani | Tomoya Hatanaka | Mami Uchida | Mikio Abe | Akihiro Mikami | Takashi Otsuki | Zhiyang Qi | Kei Harada | Michimasa Inaba | Daisuke Katagami | Hirotaka Osawa | Fujio Toriumi
Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges

We held our 5th annual AIWolf international contest to automatically play the Werewolf game “Mafia”, where players try finding liars via conversations, aiming at promoting developments in creating agents of more natural conversations in higher level, such as longer contexts, personal relationships, semantics, pragmatics, and logics, revealing the capabilities and limits of the generative AIs. In our Natural Language Division of the contest, we had six Japanese speaking agents from five teams, and three English speaking agents, to mutually run games. By using the game logs, We performed human subjective evaluations and detailed log analysis. We found that the entire system performance has largely improved over the previous year, due to the recent advantages of the LLMs. However, it is not perfect at all yet; the generated talks are sometimes inconsistent with the game actions, it is still doubtful that the agents could infer roles by logics rather than superficial utterance generations. It is not explicitly observed in this log but it would be still difficult to make an agent telling a lie, pretend as a villager but it has an opposite goal inside. Our future work includes to reveal the capability of the LLMs, whether they can make the duality of the “liar”, in other words, holding a “true” and a “false” circumstances of the agent at the same time, even holding what these circumstances look like from other agents.

2022

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Collection and Analysis of Travel Agency Task Dialogues with Age-Diverse Speakers
Michimasa Inaba | Yuya Chiba | Ryuichiro Higashinaka | Kazunori Komatani | Yusuke Miyao | Takayuki Nagai
Proceedings of the Thirteenth Language Resources and Evaluation Conference

When individuals communicate with each other, they use different vocabulary, speaking speed, facial expressions, and body language depending on the people they talk to. This paper focuses on the speaker’s age as a factor that affects the change in communication. We collected a multimodal dialogue corpus with a wide range of speaker ages. As a dialogue task, we focus on travel, which interests people of all ages, and we set up a task based on a tourism consultation between an operator and a customer at a travel agency. This paper provides details of the dialogue task, the collection procedure and annotations, and the analysis on the characteristics of the dialogues and facial expressions focusing on the age of the speakers. Results of the analysis suggest that the adult speakers have more independent opinions, the older speakers more frequently express their opinions frequently compared with other age groups, and the operators expressed a smile more frequently to the minor speakers.

2019

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Proceedings of the 1st International Workshop of AI Werewolf and Dialog System (AIWolfDial2019)
Yoshinobu Kano | Claus Aranha | Michimasa Inaba | Fujio Toriumi | Hirotaka Osawa | Daisuke Katagami | Takashi Otsuki
Proceedings of the 1st International Workshop of AI Werewolf and Dialog System (AIWolfDial2019)

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Overview of AIWolfDial 2019 Shared Task: Contest of Automatic Dialog Agents to Play the Werewolf Game through Conversations
Yoshinobu Kano | Claus Aranha | Michimasa Inaba | Fujio Toriumi | Hirotaka Osawa | Daisuke Katagami | Takashi Otsuki | Issei Tsunoda | Shoji Nagayama | Dolça Tellols | Yu Sugawara | Yohei Nakata
Proceedings of the 1st International Workshop of AI Werewolf and Dialog System (AIWolfDial2019)

2018

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Estimating User Interest from Open-Domain Dialogue
Michimasa Inaba | Kenichi Takahashi
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

Dialogue personalization is an important issue in the field of open-domain chat-oriented dialogue systems. If these systems could consider their users’ interests, user engagement and satisfaction would be greatly improved. This paper proposes a neural network-based method for estimating users’ interests from their utterances in chat dialogues to personalize dialogue systems’ responses. We introduce a method for effectively extracting topics and user interests from utterances and also propose a pre-training approach that increases learning efficiency. Our experimental results indicate that the proposed model can estimate user’s interest more accurately than baseline approaches.

2016

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Neural Utterance Ranking Model for Conversational Dialogue Systems
Michimasa Inaba | Kenichi Takahashi
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics
Ryuichiro Higashinaka | Kotaro Funakoshi | Yuka Kobayashi | Michimasa Inaba
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Dialogue breakdown detection is a promising technique in dialogue systems. To promote the research and development of such a technique, we organized a dialogue breakdown detection challenge where the task is to detect a system’s inappropriate utterances that lead to dialogue breakdowns in chat. This paper describes the design, datasets, and evaluation metrics for the challenge as well as the methods and results of the submitted runs of the participants.