Conclusion-based Counter-Argument Generation (2023)

Abstract: In real-world debates, the most common way to counter an argument is to reason against its main point, that is, its conclusion. Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. In this paper, we hypothesize that the key to effective counter-argument generation is to explicitly model the argument’s conclusion and to ensure that the stance of the generated counter is opposite to that conclusion. In particular, we propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. The approach employs a stance-based ranking component that selects the counter from a diverse set of generated candidates whose stance best opposes the generated conclusion. In both automatic and manual evaluation, we provide evidence that our approach generates more relevant and stance-adhering counters than strong baselines.

## introduction given an argument, a valid counter-argument to it should be relevant to the topic discussed by the argument while opposing to its conclusion's stance. countering the opponent's arguments in a debate effectively is key to winning the debate @xcite . while some counter-arguments attack an argument's premises or their connection to the conclusion, the most common attack is to directly rebut the argument's conclusion @xcite . the automatic countering of natural language arguments is one of the most challenging tasks in the area of computational argumentation. prior research has addressed the task through retrieval @xcite or generation-based approaches @xcite . by concept, the former requires the presence of suitable counterarguments in a predefined collection, limiting its conclusion (title): purchasing meat encourages animal abuse. premises (post): all meat, to my knowledge, is obtained by raising animals in cramped quarters and slaughtering them as soon as they are fully grown. the only exception i can think of is perhaps when you go into the woods and hunt food for yourself in which case the animal has lived an undisturbed life and is put down by humane means compared to how it happens in nature. however, this is, of course, time intensive, requires skill, expensive, and thus is of course not how the vast majority of meat is obtained. in previous work, we have studied the task of counter-argument generation through undermining weak premises in the input arguments @xcite . we assumed the input argument to be given as a set of premises and their conclusion and modeled the weakness of premises relevant to the argument conclusion. in daily-life debates, however, people often do not explicitly state their argument's main point (i.e., its conclusion), since it is often clear from the context @xcite or for rhetorical reasons, as is often the case in news editorials @xcite . this makes it challenging for computational models to generate a proper counter. state-of-the-art language models based on transformers excel in many downstream text generation tasks, such as summarization and machine translation @xcite . while they have been applied successfully for reconstructing implicit argument components such as conclusions @xcite , they still fall short on more complex tasks, such as counter-argument generation @xcite . in this paper, we investigate how to enable transformer-based language models to generate an effective counter-argument to a given argument. we observe that the performance of these models in generating relevant counters with correct stance deteriorates particularly when the input argument does not mention its conclusion. hence, we hypothesize that explicitly modeling the argument's conclusion and its stance will lead to more adequate counter-arguments. for this purpose, we propose a multitask generation approach with a stance-based ranking component. our approach jointly models the two tasks of conclusion generation and counterargument generation, and it enforces stance correctness through a stance-based ranking component. given a training dataset, where we have access to both the premises of arguments and their corresponding conclusions and counters, we explore two variations of our approach: the first shares the transformer's encoder and decoder between the two tasks, and we learn to generate both the conclusion and the counter as one sequence (separated with a special token). by contrast, the second variation is composed of one shared encoder along with two decoders, one to generate the conclusion and the other to generate the counter-argument. although we expect the trained models to often capture the stance relation between the argument and its counter, we reinforce opposite stance through a stance-based ranking component at inference time. this component samples different counter-arguments and ranks them based on their stance score towards the corresponding generated conclusion. to evaluate both approach variations, we use the changemyview dataset of @xcite , which consists of discussions where someone posts a view and others write comments opposing to this view. as in our previous work @xcite , we use a post's title as the conclusion, its body text as the premises, and each comment as a counter. to classify stance as part of our ranking component, we fine-tune roberta @xcite on a dataset of pairs of claim and counter-claim collected from the kialo.org debate platform. we com-pare our approach against two baselines; one that learns to generate the conclusion and the counterargument independently in a pipeline model and one that employs a sequence-to-sequence model but does not actively represent the conclusion. the results emphasize the deficiency of standard transformer-based models in counter-argument generation, particularly when the conclusion is not mentioned explicitly, highlighting the importance of conclusions in counter-argument generation. in most cases, our variation with shared encoder and decoder produces the best counter-arguments in terms of relevance and stance correctness. we summarize our contributions as follows: foot_0 ## related work argument generation is one of the main branches of computational argumentation, studying the synthesis of arguments in natural language texts. this field includes a host of tasks like the generation of argument conclusions @xcite , implicit premises @xcite , controlled claims @xcite , as well as the generation of counter-arguments @xcite . our work studies the task of counter-argument synthesis. the task of counter-argument synthesis has been addressed through either retrieval or generationbased approaches. an example of the former is the work of @xcite whose approach tries to retrieve relevant counters for a given argument from a collection of documents. @xcite utilized topic knowledge to retrieve the best counter for a given argument. on the other hand, generation-based approaches aim to construct counter-arguments from scratch. for example, both @xcite and @xcite worked on the task of counterclaim generation. the former developed a set of rules and classifiers to negate claims, while the latter used neural methods to learn from data. @xcite proposed an approach to generate counter-arguments by automatically identifying weak points in the input argument given the conclusion and attacking them. moreover, @xcite proposed an approach for generating long texts and applied it to the counterargument generation task. their approach relies on a retrieval component that acquires relevant key phrases for an input argument to be used to guide the generation of counter-arguments. while the size of the given argument collection limits retrieval-based approaches, the generation-based approaches either rely on the conclusion being given in the input or don't distinguish the different components in the input argumentative text. our proposed approach is generation-based, where we study the conclusion's role in the counter-argument generation task. argument conclusion is the main point an argument argues towards/against, which is important for understanding the argument. in daily life argumentation, conclusions often are left implicit @xcite . while it is easy for humans to infer the main point of an argument, it remains a challenging task for machines. hence, several works have addressed the task of conclusion inference. @xcite reconstructed implicit claim targets from argument premises using triplet neural networks. @xcite studied the effectiveness of several transformer-based models on the conclusion generation and evaluated the informativeness criteria of conclusions. @xcite utilized conclusion generation to study argument quality. our proposed approach also generates conclusions for a given argument as the first step in order to generate reliable counters. ## approach as discussed above, the conclusions of arguments are important for understanding them properly. however, they are often left implicit, making understanding hard for machines. our goal is to study how the absence of conclusions affects the performance of transformer-based counter-argument generation models. to alleviate this problem, we propose an approach that jointly learns to generate both the conclusion and the counter of an argument. at inference time, it utilizes a stance-based ranking component to select the most contrastive candidate counter in each case. we detail the generation and ranking in the following. ## experiments this section describes the experiments carried out to investigate the conclusion's importance in counter-argument generation. ## conclusion in this paper, we have studied the task of counterargument generation, considering the role of the argument's conclusion. we argued that automatically generating counter-arguments becomes more challenging when the argument's conclusion is implicit, mandating explicit modeling. to validate our claims, we have proposed an approach that jointly learns to generate the conclusion and a counter for a given argument and compare it to baselines with no explicit conclusion modeling. moreover, it explicitly enforces that the generated counters have a correct stance through a stance-based ranking component. we haved realized the approach in two ways, both using transformer-based models but with varying encoder-decoder concepts. although far from perfect, our results clearly suggest that the joint learning of the two tasks leads to better counters and to more valid conclusions of the input argument, in comparison to strong baselines. thereby, we contribute substantially towards more robused counter-argument generation.