Paper Info
Title
Transfer Learning Between Concepts For Human Behavior Modeling: An Application To Sincerity And Deception Prediction
Abstract
Transfer learning (TL) involves leveraging information from sources outside the domain at hand for enhancing model performances. Popular TL methods either directly use the data or adapt the models learned on out-of-domain resources and incorporate them within in-domain models. TL methods have shown promise in several applications such as text classification, cross domain language classification and emotion recognition. In this paper, we propose TL methods to computational human behavioral trait modeling. Many behavioral traits are abstract constructs (e.g., sincerity of an individual), and are often conceptually related to other constructs (e.g., level of deception) making TL methods an attractive option for their modeling. We consider the problem of automatically predicting human sincerity and deception from behavioral data while leveraging transfer of knowledge from each other. We compare our methods against baseline models trained only on in-domain data. Our best models achieve an Unweighted Average Recall (UAR) of 72.02% in classifying deception (baseline: 69.64%). Similarly, applied methods achieve Spearman's/Pearson's correlation values of 49.37%/48.52% between true and predicted sincerity scores (baseline: 46.51%/41.58%), indicating the success and the potential of TL for such human behavior tasks.
Year
DOI
Venue
2017
10.21437/Interspeech.2017-121
18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION
Keywords
Field
DocType
Transfer Learning (TL), sincerity prediction, deception prediction
Computer science,Cognitive science,Deception,Transfer of learning,Speech recognition,Sincerity,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
2308-457X
0
0.34
References 
Authors
9
3
Name
Order
Citations
PageRank
Qinyi Luo100.34
Rahul Gupta29218.86
Shrikanth Narayanan3123.29