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International Conference on Collaboration Technologies

CollabTech 2018: Collaboration Technologies and Social Computing pp 145–158Cite as

Analysis of Facilitators’ Behaviors in Multi-party Conversations for Constructing a Digital Facilitator System

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11000)

Abstract

In this paper, we analyze characteristics of facilitators from multi-party conversations. The goal of our study is to construct a digital facilitator system that supports consensus-building and management of conversation for high-quality discussions. Therefore, we need facilitator’s knowledge, behavior, and patterns to realize a good digital facilitator. As the 1st step for the purpose, we focus on a macro viewpoint of facilitators’ behaviors on conversation corpora. First, we generate a model based on a decision tree that classifies each participant into a facilitator or a non-facilitator, from conversation corpora. The classification accuracies by the decision trees were 0.642 and 0.737 for two corpora, respectively. The main purpose of the decision tree generation is to extract patterns from imaginable characteristics, namely features for the classifier. Therefore, next, we discuss behaviors of facilitators by analyzing the decision tree manually. In the analysis, we focus on two types of corpora; one is that each participant has a role, such as a project manager, and another is that each participant has no role in the conversation. We investigate the influence of the difference of the setting through the analysis. From the manual analysis, we obtained some common behaviors and some different behaviors about facilitators from two corpora.

Keywords

  • Facilitator’s behaviors
  • Multi-party conversation
  • Digital facilitator
  • Macro viewpoint

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Notes

  1. 1.

    This tag does not exist in the AMI corpus. Therefore, we use this feature for only the Kyutech corpus.

  2. 2.

    Note that “DA” replaces an actual dialogue act tag. For example,“IN_ratio” when the DA tag is “inform (IN).”.

  3. 3.

    These 10 facilitators were based on a subjective judgment by participants in the discussions. Hence, we checked the judgment by ourselves. The three test subjects checked the Kyutech corpus, and then voted the facilitator in each conversation. The results about eight conversations corresponded to the questionnaire of the Kyutech corpus. The rest was the conversation with the two facilitators by the questionnaire. For the conversation, the result of our judgment partially corresponded to the questionnaire. Therefore, we used the original judgment from the Kyutech corpus as the ground truth.

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Acknowledgment

This work was supported by JSPS KAKENHI Grant Number 17H01840.

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Authors and Affiliations

  1. Department of Artificial Intelligence, Kyushu Institute of Technology, 680-4 Kawazu Iizuka, Fukuoka, 820-8502, Japan

    Tsukasa Shiota, Takashi Yamamura & Kazutaka Shimada

Authors
  1. Tsukasa Shiota
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  2. Takashi Yamamura
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  3. Kazutaka Shimada
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Corresponding author

Correspondence to Kazutaka Shimada .

Editor information

Editors and Affiliations

  1. Department of Informatics, The University of Electro-Communications, Chofu, Tokyo, Japan

    Dr. Hironori Egi

  2. Graduate School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Takaya Yuizono

  3. University of Chile, Santiago, Chile

    Nelson Baloian

  4. Wakayama University, Wakayama City, Japan

    Takashi Yoshino

  5. Otsuma Women’s University, Tokyo, Japan

    Satoshi Ichimura

  6. Departamento de Informatica, Universidade Nova de Lisboa, Caparica, Portugal

    Prof. Armanda Rodrigues

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Shiota, T., Yamamura, T., Shimada, K. (2018). Analysis of Facilitators’ Behaviors in Multi-party Conversations for Constructing a Digital Facilitator System. In: Egi, H., Yuizono, T., Baloian, N., Yoshino, T., Ichimura, S., Rodrigues, A. (eds) Collaboration Technologies and Social Computing. CollabTech 2018. Lecture Notes in Computer Science(), vol 11000. Springer, Cham. https://doi.org/10.1007/978-3-319-98743-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-98743-9_12

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