BIU learning club – Dan Vilenchik – From theory to practice and back – Stance Detection as a case study

Location:

Engineering building (1103), room 329

Title:

From theory to practice and back – Stance Detection as a case study

Abstract:

Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this talk we describe a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion – we construct the interaction network from which we derive topological embeddings for each speaker. These speaker embeddings enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent speakers with opposing stances. These embeddings are then used to divide the speakers into stance-partitions. Our embedding is derived from the Semi-Definite Programming (SDP) solution to the max-cut problem on the interaction network. In this talk we shall explain how the success of our method is rooted in theoretical results in random graph theory.

We evaluated our method on three different datasets from different platforms. Our method outperforms or is comparable with supervised models, including Neural Network based approaches. 

The talk is based on a joint paper with Ron Korenblum Pick, Vladyslav Kozhukhov (students) and Oren Tsur (PI).

Paper appeared in AAAI 2022.

https://arxiv.org/abs/2112.00712

Short Bio:

Dan Vilenchik holds a PhD in computer science from Tel Aviv University. He did a postdoc at UC Berkeley and UCLA. He is currently a member of the school of Electrical Engineering at Ben-Gurion University. His research includes both theoretical (challenges of high-dimensional data) and applicative (NLP and multidisciplinary projects) aspects of machine learning.

https://www.bgu.ac.il/~vilenchi