Why Can't You Convince Me? Modeling Weaknesses in Unpersuasive Arguments

Persing, Isaac; Ng, Vincent

doi:10.24963/ijcai.2017/570

Cited by 33 publications

(34 citation statements)

References 7 publications

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“…For n-grams, the relationship to convincingness may be topic-specific, hence they are not identified as important when the model is trained on 31 different topics. The fact that MLII did not substantially shorten the length-scales for ngrams and POS n-grams corresponds to previous re-sults (Persing and Ng, 2017), which found these feature sets less informative than other argumentrelated feature sets. Table 4 also presents a breakdown of the "other" features into sentiment, ratio, count and NER features.…”

Section: Relevant Feature Determinationsupporting

confidence: 83%

Finding Convincing Arguments Using Scalable Bayesian Preference Learning

Simpson

Gurevych

2018

TACL

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We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard ratings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.

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Section: Relevant Feature Determinationsupporting

confidence: 83%

Finding Convincing Arguments Using Scalable Bayesian Preference Learning

Simpson

Gurevych

2018

TACL

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“…ness. Persing and Ng (2017) identify factors that negatively impact persuasiveness, so their factors, unlike ours, cannot explain what makes an argument persuasive. Other argument mining tasks.…”

Section: Related Workmentioning

confidence: 87%

Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays

Carlile¹,

Gurrapadi²,

Ke³

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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While argument persuasiveness is one of the most important dimensions of argumentative essay quality, it is relatively little studied in automated essay scoring research. Progress on scoring argument persuasiveness is hindered in part by the scarcity of annotated corpora. We present the first corpus of essays that are simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument's persuasiveness. This corpus could trigger the development of novel computational models concerning argument persuasiveness that provide useful feedback to students on why their arguments are (un)persuasive in addition to how persuasive they are.

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“…As a fast growing sub-field of computational argumentation mining [35,41], previous work in this area mostly work on the identification of convincing arguments [13,44] and viewpoints [14,19] from varying argumentation genres, such as social media discussions [37], political debates [4], and student essays [6]. In this line, many existing studies focus on crafting hand-made features [37,44], such as wordings and topic strengths [43,53], echoed words [2], semantic and syntactic rules [15,30], participants' personality [42], argument interactions and structure [29], and so forth. These methods, however, require labor-intensive feature engineering process, and hence have limited generalization abilities to new domains.…”

Section: Related Work 21 Argument Persuasivenessmentioning

confidence: 99%

What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process

Zeng

et al. 2020

Proceedings of the Web Conference 2020

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In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the increasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful -topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations.

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Why Can't You Convince Me? Modeling Weaknesses in Unpersuasive Arguments

Cited by 33 publications

References 7 publications

Finding Convincing Arguments Using Scalable Bayesian Preference Learning

Finding Convincing Arguments Using Scalable Bayesian Preference Learning

Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays

What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process

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