Finding enthymemes in real-world texts: A feasibility study

Razuvayevskaya, Olesya; Teufel, Simone

doi:10.3233/aac-170020

Cited by 8 publications

(8 citation statements)

References 31 publications

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“…In some cases it is sufficient to know merely the range of argumentative types used in order to grade student essays (Ong, Litman, and Brusilovsky 2014), to know what stance an essay takes toward a proposition in order to check that it provides appropriate evidence to back-up its stance (Persing and Ng 2015), or whether a claim is verifiable in order to flag these in online discussions (Park and Cardie 2014). However, if the goal is to reconstruct enthymemes (Razuvayevskaya and Teufel 2017) (see also the discussion of Feng and Hirst [2011] in Section 8.2) or ask critical questions about support relations, we also need to extract the nature of the argumentation schemes being used.…”

Section: Argument Mining: Automating Argument Analysismentioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

282

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: Argument Mining: Automating Argument Analysismentioning

confidence: 99%

Argument Mining: A Survey

Lawrence

Reed

2020

Computational Linguistics

282

186

View full text Add to dashboard Cite

show abstract

“…While there has been work on identification (i.e., classification) and reconstruction of implicit premises in enthymemes (Rajendran et al, 2016;Habernal et al, 2018;Reisert et al, 2015;Boltužić and Šnajder, 2016;Razuvayevskaya and Teufel, 2017), to our knowledge, automatically generating an implicit premise from a given enthymeme is a new task. There are two main challenges that need to be addressed: 1) lack of large scale data of incomplete arguments together with annotated missing premises needed to train a sequence-tosequence model (the largest such set contains 1.7K instances (Habernal et al, 2018)); and 2) the inherent need to model commonsense or word knowledge.…”

Section: Reasonmentioning

confidence: 99%

Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models

Chakrabarty¹,

Trivedi²,

Muresan³

2021

Preprint

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Enthymemes are defined as arguments where a premise or conclusion is left implicit. We tackle the task of generating the implicit premise in an enthymeme, which requires not only an understanding of the stated conclusion and premise, but also additional inferences that could depend on commonsense knowledge. The largest available dataset for enthymemes (Habernal et al., 2018) consists of 1.7k samples, which is not large enough to train a neural text generation model. To address this issue, we take advantage of a similar task and dataset: Abductive reasoning in narrative text (Bhagavatula et al., 2020). However, we show that simply using a state-of-the-art seq2seq model fine-tuned on this data might not generate meaningful implicit premises associated with the given enthymemes. We demonstrate that encoding discourse-aware commonsense during fine-tuning improves the quality of the generated implicit premises and outperforms all other baselines both in automatic and human evaluations on three different datasets.

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“…The task of enthymeme reconstruction has received very little attention in the AI and NLP communities [45], likely because of its complexity. As detailed in Sect.…”

Section: Overview Of Argument Explicitationmentioning

confidence: 99%

Explaining Arguments with Background Knowledge

et al. 2020

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Most information we consume as a society is obtained over the Web. News-often from questionable sources-are spread online, as are election campaigns; calls for (collective) action spread with unforeseen speed and intensity. All such actions have argumentation at their core, and the conveyed content is often strategically selected or rhetorically framed. The responsibility of critical analysis of arguments is thus tacitly transferred to the content consumer who is often not prepared for the task, nor aware of the responsibility. The ExpLAIN project aims at making the structure and reasoning of arguments explicit-not only for humans, but for Robust Argumentation Machines that are endowed with language understanding capacity. Our vision is a system that is able to deeply analyze argumentative text: that identifies arguments and counter-arguments, and reveals their internal structure, conveyed content and reasoning. A particular challenge for such a system is to uncover implicit knowledge which many arguments rely on. This requires human background knowledge and reasoning capacity, in order to explicate the complete reasoning of an argument. This article presents ongoing research of the ExpLAIN project that aims to make the vision of such a system a tangible aim. We introduce the problems and challenges we need to address, and present the progress we achieved until now by applying advanced natural language and knowledge processing methods. Our approach puts particular focus on leveraging available sources of structured and unstructured background knowledge, the automatic extension of such knowledge, the uncovering of implicit content, and reasoning techniques suitable for informal, everyday argumentation.

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Finding enthymemes in real-world texts: A feasibility study

Cited by 8 publications

References 31 publications

Argument Mining: A Survey

Argument Mining: A Survey

Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models

Explaining Arguments with Background Knowledge

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