The Utility of Discourse Parsing Features for Predicting Argumentation Structure

Hewett, Freya; Rane, Roshan Prakash; Harlacher, Nina; Stede, Manfred

doi:10.18653/v1/w19-4512

Cited by 15 publications

(10 citation statements)

References 21 publications

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“…Discourse Relations Rhetorical Structure Theory was originally developed to offer an explanation of the coherence of texts. Musi et al (2018) and, more recently Hewett et al (2019), showed that discourse relations from RST often correlate with argumentative relations. We thus derive features from RST trees and train a classifier using these features to predict an argumentative relation.…”

Section: Methodsmentioning

confidence: 99%

AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

Chakrabarty¹,

Hidey²,

Muresan³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments. Most work in argument mining has focused on modeling arguments in monologues. We propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macrolevel (argument as process) models of argumentation. Fundamentally, this approach relies on identifying relations between components of arguments in a discussion thread. Our approach for relation prediction uses contextual information in terms of fine-tuning a pretrained language model and leveraging discourse relations based on Rhetorical Structure Theory. We additionally propose a candidate selection method to automatically predict what parts of one's argument will be targeted by other participants in the discussion. Our models obtain significant improvements compared to recent state-of-the-art approaches using pointer networks and a pre-trained language model.

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Section: Methodsmentioning

confidence: 99%

AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

Chakrabarty¹,

Hidey²,

Muresan³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…Other loosely related corpora (i.e., those which have not been annotated with argumentation structures but with other linguistic phenomena) can be used in auxiliary tasks. Examples include discourse relations in the Penn Discourse TreeBank (Prasad et al 2008;Hewett et al 2019) (such as subordinating and coordinating conjunctions, adverbials, and several kinds of implicit relations) and textual entailment/natural language inference Villata 2012, 2013;Choi and Lee 2018;Conneau et al 2018).…”

Section: Practical Compatibilitymentioning

confidence: 99%

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

et al. 2023

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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.

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“…The best performance is achieved when considering a subgraph of depth 3. RST parsing is first used to analyze arguments in Microtexts by Hewett et al (2019). The texts were analyzed with multiple earlier parsers, and the one proposed by Feng and Hirst (2014) was chosen based on the manual evaluation of the results.…”

Section: Background and Related Workmentioning

confidence: 99%

Discourse-aware text classification for argument mining

Chistova¹,

Smirnov²

2022

Computational Linguistics and Intellectual Technologies

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We show that using the rhetorical structure automatically generated by the discourse parser is beneficial for paragraph-level argument mining in Russian. First, we improve the structure awareness of the current RST discourse parser for Russian by employing the recent top-down approach for unlabeled tree construction on a paragraph level. Then we demonstrate the utility of this parser in two classification argument mining subtasks of the RuARG-2022 shared task. Our approach leverages a structured LSTM module to compute a text representation that reflects the composition of discourse units in the rhetorical structure. We show that: (i) the inclusion of discourse analysis improves paragraph-level text classification; (ii) a novel TreeLSTM-based approach performs well for the computation of the complex text hidden representation using both a language model and an end-to-end RST parser; (iii) structures predicted by the proposed RST parser reflect the argumentative structures in texts in Russian.

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The Utility of Discourse Parsing Features for Predicting Argumentation Structure

Cited by 15 publications

References 21 publications

AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

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

Discourse-aware text classification for argument mining

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