A Dataset of General-Purpose Rebuttal

TL;DR

This paper introduces a new dataset and task for generating critical responses to long argumentative texts using general rebuttal arguments, enabling broader applicability across topics.

Contribution

It presents a novel dataset and method for producing topic-independent rebuttal responses to argumentative content in natural language understanding.

Findings

The dataset covers diverse topics and rebuttal strategies.

The proposed method effectively generates relevant rebuttals.

Responses outperform topic-specific approaches in generality.

Abstract

In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel task of producing a critical response to a long argumentative text, and suggest a method based on general rebuttal arguments to address it. We do this in the context of the recently-suggested task of listening comprehension over argumentative content: given a speech on some specified topic, and a list of relevant arguments, the goal is to determine which of the arguments appear in the speech. The general rebuttals we describe here (written in English) overcome the need for topic-specific arguments to be provided, by proving to be applicable for a large set of topics. This allows creating responses beyond the scope of topics for which specific arguments are available. All data collected during this work is freely available for research.