A Generic Machine Learning Based Approach for Addressee Detection In Multiparty Interaction

Addressee detection is one of the most fundamental tasks for seamless dialogue management and turn taking in human-agent interaction. Whereas addressee detection is implicit in dyadic interaction, it becomes a challenging task in multiparty interactions when more than two participants are involved. Existing research works employ either rule-based or statistical approaches for addressee detection. However, most of these works either have been tested on a single data set or only support a fixed number of participants. In this article, we propose a model based on generic features to predict the addressee in data sets with varying number of participants. The results tested on two different corpora show that the proposed model outperforms existing baselines.

[1]  A. Koller,et al.  Speech Acts: An Essay in the Philosophy of Language , 1969 .

[2]  Rieks op den Akker,et al.  Are You Being Addressed? - Real-Time Addressee Detection to Support Remote Participants in Hybrid Meetings , 2009, SIGDIAL Conference.

[3]  Koichi Shinoda,et al.  Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances , 2018, IJCAI.

[4]  Rieks op den Akker,et al.  A comparison of addressee detection methods for multiparty conversations , 2009 .

[5]  Frank Klawonn,et al.  Multi-Layer Perceptrons , 2013 .

[6]  Jean Carletta,et al.  Unleashing the killer corpus: experiences in creating the multi-everything AMI Meeting Corpus , 2007, Lang. Resour. Evaluation.

[7]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[8]  Roel Vertegaal,et al.  Look who's talking: the GAZE groupware system , 1998, CHI Conference Summary.

[9]  Antonio Torralba,et al.  Where are they looking? , 2015, NIPS.

[10]  Rodney X. Sturdivant,et al.  Applied Logistic Regression: Hosmer/Applied Logistic Regression , 2005 .

[11]  Zhi-Hua Zhou,et al.  A k-nearest neighbor based algorithm for multi-label classification , 2005, 2005 IEEE International Conference on Granular Computing.

[12]  Anton Nijholt,et al.  A corpus for studying addressing behaviour in multi-party dialogues , 2006, SIGDIAL.

[13]  Natasa Jovanovic,et al.  To whom it may concern : adressee identification in face-to-face meetings , 2007 .

[14]  Alexandre Pauchet,et al.  Performance Comparison of Machine Learning Models Trained on Manual vs ASR Transcriptions for Dialogue Act Annotation , 2018, 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI).

[15]  A. E. Eiben,et al.  Comparing parameter tuning methods for evolutionary algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[16]  Carl Vogel,et al.  Modeling Collaborative Multimodal Behavior in Group Dialogues: The MULTISIMO Corpus , 2018, LREC.

[17]  Alexandre Pauchet,et al.  AgentSlang: A fast and reliable platform for Distributed Interactive Systems , 2013, 2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing (ICCP).

[18]  Julia Hirschberg,et al.  Identifying Agreement and Disagreement in Conversational Speech: Use of Bayesian Networks to Model Pragmatic Dependencies , 2004, ACL.

[19]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[20]  Hung-Hsuan Huang,et al.  Identifying Utterances Addressed to an Agent in Multiparty Human-Agent Conversations , 2011, IVA.

[21]  C. Y. Peng,et al.  An Introduction to Logistic Regression Analysis and Reporting , 2002 .

[22]  R.P.H. Vertegaal,et al.  Look who's talking to whom : mediating joint attention in multiparty communication and collaboration , 1998 .

[23]  Irina Rish,et al.  An empirical study of the naive Bayes classifier , 2001 .

[24]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[25]  Jean Carletta,et al.  The AMI meeting corpus , 2005 .

[26]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[27]  David Traum,et al.  Evaluation of Multi-Party Reality Dialogue Interaction , 2006 .

[28]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[29]  Alexandre Pauchet,et al.  Using Multimodal Information to Enhance Addressee Detection in Multiparty Interaction , 2019, ICAART.