知能ロボティクス領域の岡田准教授らのグループがICMI 2019においてBest Paper Runner-up Award(2件)を受賞

 知能ロボティクス領域岡田 将吾准教授らのグループの論文2件が21st ACM International Conference on Multimodal Interaction(ICMI 2019)においてBest Paper Runner-up Awardを受賞しました。

 ICMIはマルチモーダルインタラクションに関する分野(視覚+聴覚など複数のモダリティの情報を統合することで理解できる現象のモデル化に関する分野)のトップカンファレンスで、Best Paper Runner-up Awardは会議で発表された論文の内、優れた論文に送られる賞(Best Paperに次ぐ優秀な論文)です。今回のICMI 2019では、138件の論文の投稿があり、うち50件の論文が採択され、この中から31件が口頭発表に選ばれました。この31件の中で7件(全投稿論文の上位5%)の論文が表彰されましたが、うち2件が岡田准教授らの研究グループの論文となります。

 ICMI 2019は、10月14日~18日にかけて中国の蘇州で開催されました。



  1. タイトル:
    Multitask Prediction of Exchange-Level Annotations for Multimodal Dialogue Systems

    Yuki Hirano (JAIST), Shogo Okada (JAIST), Haruto Nishimoto (Osaka University), Kazunori Komatani (Osaka University)    

    This paper presents multimodal computational modeling of three labels that are independently annotated per exchange to implement an adaptation mechanism of dialogue strategy in spoken dialogue systems based on recognizing user sentiment by multimodal signal processing. The three labels include (1) user's interest label pertaining to the current topic, (2) user's sentiment label, and (3) topic continuance denoting whether the system should continue the current topic or change it. Predicting the three types of labels that capture different aspects of the user's sentiment level and the system's next action contribute to adopting a dialogue strategy based on the user's sentiment. For this purpose, we enhanced shared multimodal dialogue data by annotating impressed sentiment labels and the topic continuance labels. With the corpus, we develop a multimodal prediction model for the three labels. A multitask learning technique is applied for binary classification tasks of the three labels considering the partial similarities among them.

  2. タイトル:
    Task-independent Multimodal Prediction of Group Performance Based on Product Dimensions.   

    Go Miura (JAIST), Shogo Okada (JAIST)        

    This paper proposes an approach to develop models for predicting the performance for multiple group meeting tasks, where the model has no clear correct answer. This paper adopts "product dimensions" [Hackman et al. 1967] (PD) which is proposed as a set of dimensions for describing the general properties of written passages that are generated by a group, as a metric measuring group output. This study enhanced the group discussion corpus called the MATRICS corpus including multiple discussion sessions by annotating the performance metric of PD. We extract group-level linguistic features including vocabulary level features using a word embedding technique, topic segmentation techniques, and functional features with dialog act and parts of speech on the word level. We also extracted nonverbal features from the speech turn, prosody, and head movement. With a corpus including multiple discussion data and an annotation of the group performance, we conduct two types of experiments thorough regression modeling to predict the PD.


  • 先端科学技術研究科 博士前期課程2年 平野 裕貴(受賞論文1の第一著者)
     このような賞を頂き大変光栄に思います。本研究の遂行にあたり、熱心なご指導を頂きました岡田准教授に厚く御礼申し上げます。また多くのご助言を頂きました共同研究者の駒谷教授, 西本さんに深く感謝いたします。
  • 先端科学技術研究科 博士前期課程2年 三浦 郷(受賞論文2の第一著者)