Annotating and Analyzing Semantic Role of Elementary Units and Relations in Online Persuasive Arguments

Egawa, Ryo; Morio, Gaku; Fujita, Katsuhide

doi:10.18653/v1/p19-2059

Cited by 16 publications

(12 citation statements)

References 10 publications

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“…On this data set, Niculae (2018) achieved a maximum F1-score of 0.74 for proposition classification using linear structured SVMs. Egawa, Morio, and Fujita (2019) adjust the annotation scheme of Park and Cardie slightly, replacing reference with rhetorical statement (which implicitly states the subjective value judgment by expressing figurative phrases, emotions, or rhetorical questions) and replacing the relations with the more standard attack and support. This scheme was then used to annotate 345 posts from the ChangeMyView sub-Reddit, 32 resulting in 4,612 proposition classifications and 2,713 relations that were then used in analyzing the semantic role of persuasive arguments.…”

Section: Intrinsic Clausal Propertiesmentioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

281

186

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Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.

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Section: Intrinsic Clausal Propertiesmentioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

281

186

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“…As Lawrence and Reed (2019) state, "one of the challenges faced by current approaches to argument mining is the lack of large quantities of appropriately annotated arguments to serve as training and test data." Since the availability of labeled corpora is crucial for designing, training and evaluating AM algorithms, numerous prior works have dealt with creating annotated data sets, such as the Araucaria corpus (Reed et al, 2008b), the European Court of Human Rights (ECHR) corpus of , the Debatepedia corpus (Cabrio and Villata, 2012), the ChangeMyView corpus (Egawa et al, 2019) or the persuasive essays corpus of Stab and Gurevych (2014a) with 90 essays and the corpus of Stab and Gurevych (2017a) with 402 persuasive student essays. These corpora have been widely used for various AM tasks, such as the identification of argument components (Rooney et al, 2012), corpus wide AM (Ein-Dor et al, 2019) or end-to-end AM (Persing and Ng, 2016).…”

Section: Argument Annotated Corpora and Annotation Schemesmentioning

confidence: 99%

A Corpus for Argumentative Writing Support in German

Wambsganss¹,

Niklaus²,

Söllner³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language. We propose an annotation scheme based on annotation guidelines that allows to model claims and premises as well as support and attack relations for capturing the structure of argumentative discourse in student-written peer reviews. We conduct an annotation study with three annotators on 50 persuasive essays to evaluate our annotation scheme. The obtained interrater agreement of α = 0.57 for argument components and α = 0.49 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to moderate agreement. Finally, we present our freely available corpus of 1,000 persuasive student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of argumentative writing support systems for students.

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“…To get a better understanding of the persuasiveness of requests on crowdfunding platforms, Yang et al [13] presented a hierarchical neural network in a semi-supervised fashion to make the persuasiveness quantifiable. Egawa et al [14] demonstrated five types of elementary units and two types of relations to characterize persuasive arguments and proposed an annotation scheme to capture the semantic roles of arguments in an online persuasive forum [15,3,4]. Furthermore, Hidey and McKeown [16] proposed a neural model with words, discourse relations, and semantic frames to predict persuasiveness in social media.…”

Section: Related Workmentioning

confidence: 99%

Persuasive Dialogue Understanding: the Baselines and Negative Results

Chen

Ghosal

Majumder

et al. 2020

Preprint

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Persuasion aims at forming one's opinion and action via a series of persuasive messages containing persuader's strategies. Due to its potential application in persuasive dialogue systems, the task of persuasive strategy recognition has gained much attention lately. Previous methods on user intent recognition in dialogue systems adopt recurrent neural network (RNN) or convolutional neural network (CNN) to model context in conversational history, neglecting the tactic history and intra-speaker relation. In this paper, we demonstrate the limitations of a Transformer-based approach coupled with Conditional Random Field (CRF) for the task of persuasive strategy recognition. In this model, we leverage inter-and intra-speaker contextual semantic features, as well as label dependencies to improve the recognition. Despite extensive hyper-parameter optimizations, this architecture fails to outperform the baseline methods. We observe two negative results. Firstly, CRF cannot capture persuasive label dependencies, possibly as strategies in persuasive dialogues do not follow any strict grammar or rules as the cases in Named Entity Recognition (NER) or part-of-speech (POS) tagging. Secondly, the Transformer encoder trained from scratch is less capable of capturing sequential information in persuasive dialogues than Long Short-Term Memory (LSTM). We attribute this to the reason that the vanilla Transformer encoder does not efficiently consider relative position information of sequence elements.

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Annotating and Analyzing Semantic Role of Elementary Units and Relations in Online Persuasive Arguments

Cited by 16 publications

References 10 publications

Argument Mining: A Survey

Argument Mining: A Survey

A Corpus for Argumentative Writing Support in German

Persuasive Dialogue Understanding: the Baselines and Negative Results

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