Warrants Generations using a Language Model and a Multi-Agent System

Abstract

Each argument begins with a conclusion, which is followed by one or more premises supporting the conclusion. The warrant is a critical component of Toulmin's argument model; it explains why the premises support the claim. Despite its critical role in establishing the claim's veracity, it is frequently omitted or left implicit, leaving readers to infer. We consider the problem of producing more diverse and high-quality warrants in response to a claim and evidence. To begin, we employ BART [1] as a conditional sequence tosequence language model to guide the output generation process. On the ARCT dataset [2], we fine-tune the BART model. Second, we propose the Multi-Agent Network for Warrant Generation as a model for producing more diverse and high-quality warrants by combining Reinforcement Learning (RL) and Generative Adversarial Networks (GAN) with the mechanism of mutual awareness of agents. In terms of warrant generation, our model generates a greater variety of warrants than other baseline models. The experimental results validate the effectiveness of our proposed hybrid model for generating warrants.