We present ACME: A Chatbot for asylum-seeking Migrants tool that goes beyond the checklists used for handling well-defined, in Europe. ACME relies on computational argumentation and simple procedures since there is not only a problem of evaluating aims to help migrants identify the highest level of protection they legal and factual data, but there is also an issue with understanding can apply for. This would contribute to a more sustainable migration which procedures are relevant. Indeed, there is not only one type of by reducing the load on territorial commissions, Courts, and humanitarian protection but several ones. Importantly, since applicants may be political organizations supporting asylum applicants. We describe the refugees and victims of abuse, discrimination, and persecution, context, system architectures, technologies, and the case study used the collection and processing of their personal data for immigration to run the demonstration.

Recent years witnessed significant performance advancements in deep-learning-driven natural language models, with a strong focus on the development and release of Large Language Models (LLMs). These improvements resulted in better quality AI-generated output but rely on resource-expensive training and upgrading of models. Although different studies have proposed a range of techniques to enhance LLMs without retraining, none have considered computational argumentation as an option. This is a missed opportunity since computational argumentation is an intuitive mechanism that formally captures agents' interactions and the information conflict that may arise during such interplays, and so it seems well-suited for boosting the reasoning and conversational abilities of LLMs in a seamless manner. In this paper, we present a pipeline (MQArgEng) and preliminary study to evaluate the effect of introducing computational argumentation semantics on the performance of LLMs. Our experiment's goal was to provide a proof-of-concept and a feasibility analysis in order to foster (or deter) future research towards a fully-fledged argumentation engine plugin for LLMs. Exploratory results using the MT-Bench indicate that MQArgEng provides a moderate performance gain in most of the examined topical categories and, as such, show promise and warrant further research.

Computational argumentation has become an essential tool in various fields, including artificial intelligence, law, and public policy. It is an emerging research field in natural language processing (NLP) that attracts increasing attention. Research on computational argumentation mainly involves two types of tasks: argument mining and argument generation. As large language models (LLMs) have demonstrated strong abilities in understanding context and generating natural language, it is worthwhile to evaluate the performance of LLMs on various computational argumentation tasks. This work aims to embark on an assessment of LLMs, such as ChatGPT, Flan models and LLaMA2 models, under zero-shot and few-shot settings within the realm of computational argumentation. We organize existing tasks into 6 main classes and standardise the format of 14 open-sourced datasets. In addition, we present a new benchmark dataset on counter speech generation, that aims to holistically evaluate the end-to-end performance of LLMs on argument mining and argument generation. Extensive experiments show that LLMs exhibit commendable performance across most of these datasets, demonstrating their capabilities in the field of argumentation. We also highlight the limitations in evaluating computational argumentation and provide suggestions for future research directions in this field.

This paper examines the interdisciplinary research question of how to integrate Computational Argumentation, as studied in AI, with Cognition, as can be found in Cognitive Science, Linguistics, and Philosophy. It stems from the work of the 1st Workshop on Computational Argumentation and Cognition (COGNITAR), which was organized as part of the 24th European Conference on Artificial Intelligence (ECAI), and took place virtually on September 8th, 2020. The paper begins with a brief presentation of the scientific motivation for the integration of Computational Argumentation and Cognition, arguing that within the context of Human-Centric AI the use of theory and methods from Computational Argumentation for the study of Cognition can be a promising avenue to pursue. A short summary of each of the workshop presentations is given showing the wide spectrum of problems where the synthesis of the theory and methods of Computational Argumentation with other approaches that study Cognition can be applied. The paper presents the main problems and challenges in the area that would need to be addressed, both at the scientific level but also at the epistemological level, particularly in relation to the synthesis of ideas and approaches from the various disciplines involved.

Dialogue systems are widely used in AI to support timely and interactive communication with users. We propose a general-purpose dialogue system architecture that leverages computational argumentation to perform reasoning and provide consistent and explainable answers. We illustrate the system using a COVID-19 vaccine information case study.

What's going on: The field that works on such systems is called "computational argumentation." This summer, a roomful of journalists watched a demonstration in which two human debaters argued with a talking computer, produced by IBM, that used AI-infused computational argumentation to deliver speeches and rebut its opponents' claims. Axios spoke with six researchers behind IBM's "Project Debater" to learn how it works, and to track the state of the art in language understanding. The context: AI researchers are prone to using games to measure their progress. In 2011, Watson beat a pair of humans at Jeopardy.

Siri or Alexa can get you the weather, but don't expect a conversation. Neither can chatbots, once the next big thing, hold a back-and-forth. But researchers are now developing systems that leapfrog chit-chat to the next frontier: They can argue and play devil's advocate, Axios' Kaveh Waddell writes. Why it matters: Researchers are striving to make machines that can converse knowledgeably with humans and explain how they reach their conclusions. In the absence of advanced AI that can think intelligently, this system is another step in that direction.

Argumentation is a form of reasoning that makes explicit the reasons for the conclusions that are drawn and how con- flicts between reasons are resolved. This provides a natural mechanism, for example, to handle inconsistent and uncer- tain information and to resolve conflicts of opinion between intelligent agents. The advantage of a mechanism based on argumentation is that considering the reasons behind the conclusions offers more than considering the conclusions alone (to adapt something Isaac Bashevis Singer once said, the approach has “more vitamins” than other approaches to reasoning). For example, in dealing with inconsistent infor- mation, an early use of argumentation, it is possible to know more than just that we have the inconsistent conclusions p and not p. We can establish exactly which pieces of infor- mation lead to these conclusions and can then prioritize one conclusion over another on the basis of this information, de- cide what information should be revised to achieve consis- tency, or even determine what additional investigation needs to be carried out (when we have reason to believe both that it is raining outside and not raining outside, and have no way of determining which is correct, going to look may be the best solution).