Retrieval of the Best Counterargument without Prior Topic Knowledge

Abstract

Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments' premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60% accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time.

ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario

“…the same argumentative aspect, smart AM decision-supporting systems should provide end-users with argument clusters rather than unsorted lists of arguments. Multiple lines of research have addressed this problem, including unsupervised learning of semantic similarities of arguments [3,27].…”

A Dataset of General-Purpose Rebuttal

“…In the context of computational argumentation much attention has been given to mapping rebuttal or disagreement among arguments. Such works include datasets exemplifying these relations (Walker et al, 2012;Peldszus and Stede, 2015a;Musi et al, 2017), modeling them (Sridhar et al, 2015) and explicitly detecting them (Rosenthal and McKeown, 2015;Peldszus and Stede, 2015b;Wachsmuth et al, 2018). The GPR-KB in this work is reminiscent of argument datasets that depict rebuttal relations, but the arguments are of a different type, being manually authored as general and applicable to a wide range of topics.…”

Detecting Attackable Sentences in Arguments

“…Effectively refuting an argument is an important skill in persuasion dialogue, and the first step is to find appropriate points to attack in the argument. Prior work in NLP has studied argument quality (Wachsmuth et al, 2017a;Habernal and Gurevych, 2016a) and counterargument generation (Hua et al, 2019;Wachsmuth et al, 2018). But these studies mainly concern an argument's overall quality and making counterarguments toward the main claim, without investigating what parts of an argument are attackable for successful persuasion.…”

“…NLP researchers have widely studied the effectiveness of counterarguments on persuasion (Tan et al, 2016;Cano-Basave and He, 2016;Wei et al, 2016;Wang et al, 2017;Morio et al, 2019) and how to generate counterarguments (Hua et al, 2019;Wachsmuth et al, 2018). Most of the work focuses on the characteristics of counterarguments with respect to topics and styles, without consideration of what points to attack.…”