Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Parsing Argumentation Structures in Persuasive Essays

In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using integer linear programming. We show that our model considerably improves the performance of base classifiers and significantly outperforms challenging heuristic baselines. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement. This corpus and the annotation guidelines are freely available for ensuring reproducibility and to encourage future research in computational argumentation.Comment: Under review in Computational Linguistics. First submission: 26 October 2015. Revised submission: 15 July 201

Argumentation models and their use in corpus annotation: practice, prospects, and challenges

The study of argumentation is transversal to several research domains, from philosophy to linguistics, from the law to computer science and artificial intelligence. In discourse analysis, several distinct models have been proposed to harness argumentation, each with a different focus or aim. To analyze the use of argumentation in natural language, several corpora annotation efforts have been carried out, with a more or less explicit grounding on one of such theoretical argumentation models. In fact, given the recent growing interest in argument mining applications, argument-annotated corpora are crucial to train machine learning models in a supervised way. However, the proliferation of such corpora has led to a wide disparity in the granularity of the argument annotations employed. In this paper, we review the most relevant theoretical argumentation models, after which we survey argument annotation projects closely following those theoretical models. We also highlight the main simplifications that are often introduced in practice. Furthermore, we glimpse other annotation efforts that are not so theoretically grounded but instead follow a shallower approach. It turns out that most argument annotation projects make their own assumptions and simplifications, both in terms of the textual genre they focus on and in terms of adapting the adopted theoretical argumentation model for their own agenda. Issues of compatibility among argument-annotated corpora are discussed by looking at the problem from a syntactical, semantic, and practical perspective. Finally, we discuss current and prospective applications of models that take advantage of argument-annotated corpora

Annotating argumentative structure in English-as-a-Foreign-Language learner essays

AbstractArgument mining (AM) aims to explain how individual argumentative discourse units (e.g. sentences or clauses) relate to each other and what roles they play in the overall argumentation. The automatic recognition of argumentative structure is attractive as it benefits various downstream tasks, such as text assessment, text generation, text improvement, and summarization. Existing studies focused on analyzing well-written texts provided by proficient authors. However, most English speakers in the world are non-native, and their texts are often poorly structured, particularly if they are still in the learning phase. Yet, there is no specific prior study on argumentative structure in non-native texts. In this article, we present the first corpus containing argumentative structure annotation for English-as-a-foreign-language (EFL) essays, together with a specially designed annotation scheme. The annotated corpus resulting from this work is called “ICNALE-AS” and contains 434 essays written by EFL learners from various Asian countries. The corpus presented here is particularly useful for the education domain. On the basis of the analysis of argumentation-related problems in EFL essays, educators can formulate ways to improve them so that they more closely resemble native-level productions. Our argument annotation scheme is demonstrably stable, achieving good inter-annotator agreement and near-perfect intra-annotator agreement. We also propose a set of novel document-level agreement metrics that are able to quantify structural agreement from various argumentation aspects, thus providing a more holistic analysis of the quality of the argumentative structure annotation. The metrics are evaluated in a crowd-sourced meta-evaluation experiment, achieving moderate to good correlation with human judgments.</jats:p

Mining arguments in scientific abstracts: Application to argumentative quality assessment

Argument mining consists in the automatic identification of argumentative structures in natural language, a task that has been recognized as particularly challenging in the scientific domain. In this work we propose SciARG, a new annotation scheme, and apply it to the identification of argumentative units and relations in abstracts in two scientific disciplines: computational linguistics and biomedicine, which allows us to assess the applicability of our scheme to different knowledge fields. We use our annotated corpus to train and evaluate argument mining models in various experimental settings, including single and multi-task learning. We investigate the possibility of leveraging existing annotations, including discourse relations and rhetorical roles of sentences, to improve the performance of argument mining models. In particular, we explore the potential offered by a sequential transfer- learning approach in which supplementary training tasks are used to fine-tune pre-trained parameter-rich language models. Finally, we analyze the practical usability of the automatically-extracted components and relations for the prediction of argumentative quality dimensions of scientific abstracts.Agencia Nacional de Investigación e InnovaciónMinisterio de Economía, Industria y Competitividad (España

Towards an authentic argumentation literacy test

A central goal of education is to improve argumentation literacy. How do we know how well this goal is achieved? Can we measure argumentation literacy? The present study is a preliminary step towards measuring the efficacy of education with regards to argumentation literacy. Tests currently in use to determine critical thinking skills are often similar to IQ-tests in that they predominantly measure logical and mathematical abilities. Thus, they may not measure the various other skills required in understanding authentic argumentation. To identify the elements of argumentation literacy, this exploratory study begins by surveying introductory textbooks within argumentation theory, critical thinking, and rhetoric. Eight main abilities have been identified. Then, the study outlines an Argumentation Literacy Test that would comprise these abilities suggested by the literature. Finally, the study presents results from a pilot of a version of such a test and discusses needs for further development

Context-aware Argument Mining and Its Applications in Education

Context is crucial for identifying arguments and argumentative relations in text, but existing argument studies have not addressed context dependence adequately. In this thesis, we propose context-aware argument mining that makes use of contextual features extracted from writing topics and context sentences to improve state-of-the-art argument component and argumentative relation classifications. The effectiveness as well as generality of our proposed contextual features is proven through its application in different argument mining tasks in student essays. We further evaluate the applicability of our proposed argument mining models in automated persuasive essay scoring tasks