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

Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!

Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at \url{http://github.com/UKPLab/coling2018-xling_argument_mining}.Comment: Accepted at Coling 201

Argument Annotated Essays

The corpus consists of argument annotated persuasive essays including annotations of argument components and argumentative relations

Opposing Arguments in Persuasive Essays

This corpus includes 402 persuasive essays. Each essay is annotated as “positive” if it includes an opposing argument and “negative” if it includes only arguments supporting the author's standpoint

Argumentative Writing Support by means of Natural Language Processing

Persuasive essay writing is a powerful pedagogical tool for teaching argumentation skills. So far, the provision of feedback about argumentation has been considered a manual task since automated writing evaluation systems are not yet capable of analyzing written arguments. Computational argumentation, a recent research field in natural language processing, has the potential to bridge this gap and to enable novel argumentative writing support systems that automatically provide feedback about the merits and defects of written arguments. The automatic analysis of natural language arguments is, however, subject to several challenges. First of all, creating annotated corpora is a major impediment for novel tasks in natural language processing. At the beginning of this research, it has been mostly unknown whether humans agree on the identification of argumentation structures and the assessment of arguments in persuasive essays. Second, the automatic identification of argumentation structures involves several interdependent and challenging subtasks. Therefore, considering each task independently is not sufficient for identifying consistent argumentation structures. Third, ordinary arguments are rarely based on logical inference rules and are hardly ever in a standardized form which poses additional challenges to human annotators and computational methods. To approach these challenges, we start by investigating existing argumentation theories and compare their suitability for argumentative writing support. We derive an annotation scheme that models arguments as tree structures. For the first time, we investigate whether human annotators agree on the identification of argumentation structures in persuasive essays. We show that human annotators can reliably apply our annotation scheme to persuasive essays with substantial agreement. As a result of this annotation study, we introduce a unique corpus annotated with fine-grained argumentation structures at the discourse-level. Moreover, we pre- sent a novel end-to-end approach for parsing argumentation structures. We identify the boundaries of argument components using sequence labeling at the token level and propose a novel joint model that globally optimizes argument component types and argumentative relations for identifying consistent argumentation structures. We show that our model considerably improves the performance of local base classifiers and significantly outperforms challenging heuristic baselines. In addition, we introduce two approaches for assessing the quality of natural language arguments. First, we introduce an approach for identifying myside biases which is a well-known tendency to ignore opposing arguments when formulating arguments. Our experimental results show that myside biases can be recognized with promising accuracy using a combination of lexical features, syntactic features and features based on adversative transitional phrases. Second, we investigate for the first time the characteristics of insufficiently supported arguments. We show that insufficiently supported arguments frequently exhibit specific lexical indicators. Moreover, our experimental results indicate that convolutional neural networks significantly outperform several challenging baselines

Insufficiently Supported Arguments in Argumentative Essays

This corpus includes 1029 arguments taken from argumentative essays. Each argument is annotated as “insufficient” if its premises do not provide enough evidence for accepting or rejecting the claim

Debating Technology for Dialogical Argument:Sensemaking, Engagement and Analytics

Debating technologies, a newly emerging strand of research into computational technologies to support human debating, offer a powerful way of providing naturalistic, dialogue-based interaction with complex information spaces. The full potential of debating technologies for dialogical argument can, however, only be realized once key technical and engineering challenges are overcome, namely data structure, data availability, and interoperability between components. Our aim in this article is to show that the Argument Web, a vision for integrated, reusable, semantically rich resources connecting views, opinions, arguments, and debates online, offers a solution to these challenges. Through the use of a running example taken from the domain of citizen dialogue, we demonstrate for the first time that different Argument Web components focusing on sensemaking, engagement, and analytics can work in concert as a suite of debating technologies for rich, complex, dialogical argument