Siamese Neural Network for Same Side Stance Classification

authored by

Milad Alshomary, Henning Wachsmuth

Abstract

Classifying the stance of an argument towards its target is an important step in many applications of computational argumentation. A simpler variant of stance classification was proposed as a shared task recently, called sameside stance classification: Given two arguments on the same topic, decided whether they have the same stance. In this paper, we present our approach to the shared task, exploring the potential of modeling same-side stance as a similarity learning task. For this purpose, we train a siamese neural network on pairs of arguments represented in an embedding space. In the two scenarios of the shared task, within topics and cross topics, our approach achieved an accuracy of 0.53 and 0.56 respectively.

External Organisation(s)

Paderborn University

Type

Conference contribution

Pages

12-16

No. of pages

5

Publication date

2019

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computer Science(all)

Electronic version(s)

https://ceur-ws.org/Vol-2921/paper1.pdf (Access: Open)