Work

Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations

Year: 2020

Type: conference-paper

Abstract: Stance detection is an important component of understanding hidden influences in everyday life.Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples.In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets.Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/2020.emnlp-main.717

Authors: Emily Allaway, Kathleen McKeown

Institution: Columbia University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Defense Advanced Research Projects Agency

Awards: G4713059963, G4818418728