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Timing Prediction of Facilitating Utterance in Multi-party Conversation

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1215)


Supporting consensus-building in multi-party conversations is a very important task in intelligent systems. To conduct smooth, active, and productive discussions, we need a facilitator who controls a discussion appropriately. However, it is impractical to assign a good facilitator to each group in the discussion environment. The goal of our study is to develop a digital facilitator system that supports high-quality discussions. One role of the digital facilitator is to generate facilitating utterances in the discussions. To realize the system, we need to predict the timing of facilitating utterances. To apply a machine learning technique to our model, we construct a data set from the AMI corpus, first. For the construction, we use some rules based on the annotation of the corpus. Then, we generate a prediction model with verbal and non-verbal features extracted from discussions. We obtained 0.75 on the F-measure. We compared our model with a baseline method. Our model outperformed the baseline (0.7 vs. 0.5 on the AUC value). In addition, we introduce additional features about the role of participants in the AMI corpus. By using the additional features, the F measure increased by 2 points. The experimental results show the effectiveness of our model.


  • Multi-party conversation
  • Timing prediction
  • Facilitation

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Fig. 1.
Fig. 2.
Fig. 3.


  1. 1.

    Five is the mode value of utterances with the Gatekeeper and specific DA tags.

  2. 2.

    For overlaps, we do not handle this feature because the overlap length is usually shorter as compared with the silence length in discussion.

  3. 3.

    Note that our model did not use any DA tags as features.


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

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Correspondence to Kazutaka Shimada .

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Sembokuya, T., Shimada, K. (2020). Timing Prediction of Facilitating Utterance in Multi-party Conversation. In: Nguyen, LM., Phan, XH., Hasida, K., Tojo, S. (eds) Computational Linguistics. PACLING 2019. Communications in Computer and Information Science, vol 1215. Springer, Singapore.

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