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M2P2: Multimodal Persuasion Prediction using Adaptive Fusion
arXiv - CS - Multimedia Pub Date : 2020-06-03 , DOI: arxiv-2006.11405 Chongyang Bai, Haipeng Chen, Srijan Kumar, Jure Leskovec, V.S. Subrahmanian
arXiv - CS - Multimedia Pub Date : 2020-06-03 , DOI: arxiv-2006.11405 Chongyang Bai, Haipeng Chen, Srijan Kumar, Jure Leskovec, V.S. Subrahmanian
Identifying persuasive speakers in an adversarial environment is a critical
task. In a national election, politicians would like to have persuasive
speakers campaign on their behalf. When a company faces adverse publicity, they
would like to engage persuasive advocates for their position in the presence of
adversaries who are critical of them. Debates represent a common platform for
these forms of adversarial persuasion. This paper solves two problems: the
Debate Outcome Prediction (DOP) problem predicts who wins a debate while the
Intensity of Persuasion Prediction (IPP) problem predicts the change in the
number of votes before and after a speaker speaks. Though DOP has been
previously studied, we are the first to study IPP. Past studies on DOP fail to
leverage two important aspects of multimodal data: 1) multiple modalities are
often semantically aligned, and 2) different modalities may provide diverse
information for prediction. Our M2P2 (Multimodal Persuasion Prediction)
framework is the first to use multimodal (acoustic, visual, language) data to
solve the IPP problem. To leverage the alignment of different modalities while
maintaining the diversity of the cues they provide, M2P2 devises a novel
adaptive fusion learning framework which fuses embeddings obtained from two
modules -- an alignment module that extracts shared information between
modalities and a heterogeneity module that learns the weights of different
modalities with guidance from three separately trained unimodal reference
models. We test M2P2 on the popular IQ2US dataset designed for DOP. We also
introduce a new dataset called QPS (from Qipashuo, a popular Chinese debate TV
show ) for IPP. M2P2 significantly outperforms 3 recent baselines on both
datasets. Our code and QPS dataset can be found at
http://snap.stanford.edu/m2p2/.
更新日期:2020-06-23