Corpus Wide Argument Mining -- a Working Solution

One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates

Exploring the Potential of Large Language Models in Computational Argumentation

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

Knowledge acquisition and corpus for argumentation-based chatbots

Many of the conversations we have every day involve exchanges of arguments and counteraguments. In the context of artificial intelligence and argumentation theory, such phenomena fall into the area of dialogical argumentation. Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogical argumentation. We can assume that a chatbot would take a particular stance in the dialogue, opposing the stance of the user. In order to succeed, the chatbot also needs to be aware of various arguments and the interplay between them. Such knowledge can be represented by a directed graph, where nodes stand for arguments and arcs symbolise conflicts between them. The chatbot must be aware of both sides of the discussion, i.e. the arguments that it can play as well as ones that the user might have, to be able to formulate convincing responses. The availability of large argument graphs for research, however, is very limited. This means that researchers do not have corpora available which hinders the development of new chatbots and limits the e↵ectiveness of existing ones. In this paper, we propose a method to acquire a large number of arguments in a graph structure using crowd sourcing. We evaluate this method in a study with participants and present a corpus which can be used for further research in computational argumentation and chatbot technologies for argumentation

Sentence-Level Content Planning and Style Specification for Neural Text Generation

Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as separate problems. Recent all-in-one style neural generation models have made impressive progress, yet they often produce outputs that are incoherent and unfaithful to the input. To address these issues, we present an end-to-end trained two-step generation model, where a sentence-level content planner first decides on the keyphrases to cover as well as a desired language style, followed by a surface realization decoder that generates relevant and coherent text. For experiments, we consider three tasks from domains with diverse topics and varying language styles: persuasive argument construction from Reddit, paragraph generation for normal and simple versions of Wikipedia, and abstract generation for scientific articles. Automatic evaluation shows that our system can significantly outperform competitive comparisons. Human judges further rate our system generated text as more fluent and correct, compared to the generations by its variants that do not consider language style.Comment: Accepted as a long paper to EMNLP 201

The Effect of Graphic Organizers and Instructional Scaffolding on Argumentative Writing Performance Among TESL Undergraduates

The present study investigated the effect of graphic organizers and instructional scaffolding on argumentative writing performance among TESL undergraduates. The study employed a quasi-experimental research using the pre-test and post-test design involving 90 TESL undergraduates being placed equally in three different groups underwent lessons on argumentative essay writing using different delivery modes, modes comprising of four stages of learning for a duration of four weeks. During the intervention period, three small groups of TESL undergraduates from the GOIS and GONI delivery modes were video-recorded to investigate on how they communicate in their groups. After the intervention, a semi-structured interview was carried out. A total of 9 students (GOIS, n=3; GONI, n=3; NGNI, n=3) were interviewed and the interviews were audio-recorded. The one-way ANCOVA was used to analyse the argumentative writing performance among the TESL undergraduates. The percentages were used to compare the overall percentages of Comm between the GOIS and GONI delivery modes while the qualitative data from the semi-structured interview of the three delivery modes were analysed using the constant comparative approach. Results showed that the group which underwent the GOIS delivery mode performed significantly better in the overall argumentative essay writing performance (p<.05) compared to their counterparts in the GONI and NGNI delivery modes. Additionally, in terms of the overall frequency of conjunctions and overall frequency of argumentative elements, the results indicated that both the GOIS and GONI groups performed significantly better (p<.05) than the NGNI group. In terms of overall percentages of , the GONI group outperformed the GOIS group. The findings from the semi-structured interview revealed that the GOIS group experienced learning better compared to the GONI and NGNI groups. The research confirmed that the GOIS and GONI delivery modes are effective in enhancing argumentative writing performance among TESL undergraduates. In line with this, the research ends with a recommendation for educators to adopt these delivery modes in t argumentative writing skills are enhanced. (Abstract by Author