{"title":"从头部运动特征和语言特征识别对话者的行为及其时间与印象形成有关","authors":"Shumpei Otsuchi, Koya Ito, Yoko Ishii, Ryo Ishii, Shinichirou Eitoku, Kazuhiro Otsuka","doi":"10.1145/3577190.3614124","DOIUrl":null,"url":null,"abstract":"A prediction-explanation framework is proposed to identify when and what behaviors are involved in forming interlocutors’ impressions in group discussions. We targeted the self-reported scores of 16 impressions, including enjoyment and concentration. To that end, we formulate the problem as discovering behavioral features that contributed to the impression prediction and determining the timings that the behaviors frequently occurred. To solve this problem, this paper proposes a two-fold framework consisting of the prediction part followed by the explanation part. The former prediction part employs random forest regressors using functional head-movement features and BERT-based linguistic features, which can capture various aspects of interactive conversational behaviors. The later part measures the levels of features’ contribution to the prediction using a SHAP analysis and introduces a novel idea of temporal decomposition of features’ contributions over time. The influential behaviors and their timings are identified from local maximums of the temporal distribution of features’ contributions. Targeting 17-group 4-female discussions, the predictability and explainability of the proposed framework are confirmed by some case studies and quantitative evaluations of the detected timings.","PeriodicalId":93171,"journal":{"name":"Companion Publication of the 2020 International Conference on Multimodal Interaction","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying Interlocutors' Behaviors and its Timings Involved with Impression Formation from Head-Movement Features and Linguistic Features\",\"authors\":\"Shumpei Otsuchi, Koya Ito, Yoko Ishii, Ryo Ishii, Shinichirou Eitoku, Kazuhiro Otsuka\",\"doi\":\"10.1145/3577190.3614124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A prediction-explanation framework is proposed to identify when and what behaviors are involved in forming interlocutors’ impressions in group discussions. We targeted the self-reported scores of 16 impressions, including enjoyment and concentration. To that end, we formulate the problem as discovering behavioral features that contributed to the impression prediction and determining the timings that the behaviors frequently occurred. To solve this problem, this paper proposes a two-fold framework consisting of the prediction part followed by the explanation part. The former prediction part employs random forest regressors using functional head-movement features and BERT-based linguistic features, which can capture various aspects of interactive conversational behaviors. The later part measures the levels of features’ contribution to the prediction using a SHAP analysis and introduces a novel idea of temporal decomposition of features’ contributions over time. The influential behaviors and their timings are identified from local maximums of the temporal distribution of features’ contributions. Targeting 17-group 4-female discussions, the predictability and explainability of the proposed framework are confirmed by some case studies and quantitative evaluations of the detected timings.\",\"PeriodicalId\":93171,\"journal\":{\"name\":\"Companion Publication of the 2020 International Conference on Multimodal Interaction\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Companion Publication of the 2020 International Conference on Multimodal Interaction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3577190.3614124\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion Publication of the 2020 International Conference on Multimodal Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3577190.3614124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identifying Interlocutors' Behaviors and its Timings Involved with Impression Formation from Head-Movement Features and Linguistic Features
A prediction-explanation framework is proposed to identify when and what behaviors are involved in forming interlocutors’ impressions in group discussions. We targeted the self-reported scores of 16 impressions, including enjoyment and concentration. To that end, we formulate the problem as discovering behavioral features that contributed to the impression prediction and determining the timings that the behaviors frequently occurred. To solve this problem, this paper proposes a two-fold framework consisting of the prediction part followed by the explanation part. The former prediction part employs random forest regressors using functional head-movement features and BERT-based linguistic features, which can capture various aspects of interactive conversational behaviors. The later part measures the levels of features’ contribution to the prediction using a SHAP analysis and introduces a novel idea of temporal decomposition of features’ contributions over time. The influential behaviors and their timings are identified from local maximums of the temporal distribution of features’ contributions. Targeting 17-group 4-female discussions, the predictability and explainability of the proposed framework are confirmed by some case studies and quantitative evaluations of the detected timings.