Working on the argument pipeline: Through flow issues between natural           language argument, instantiated arguments, and argumentation frameworks

Wyner, Adam; Engers, Tom van; Hunter, Anthony

doi:10.3233/aac-160002

Cited by 11 publications

(11 citation statements)

References 35 publications

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“…It has been claimed that for reconstructing and evaluating natural language arguments, one has to fully 'roll out' their implicit premises (van Eemeren et al, 2014, Chap. 3.2) and leverage knowledge bases (Wyner et al, 2016). We believe that a system that can distinguish between the wrong and the right warrant given its context will be helpful in filtering out good candidates in argument reconstruction.…”

Section: Discussionmentioning

confidence: 99%

The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants

Habernal¹,

Wachsmuth²,

Gurevych³

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

123

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Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. 1 On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.

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Section: Discussionmentioning

confidence: 99%

The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants

Habernal¹,

Wachsmuth²,

Gurevych³

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

123

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“…Summarizing, in this work, which develops from [32,96,103,107], we motivate and present a novel argumentation inspired semantics of theories consisting of defeasible rules which avoids some of the semantic limitations and computational pitfalls of alternative argumentation based accounts. Moreover, we report on initial progress to tie our formal model of argumentation to a controlled natural language allowing for expression of potentially incomplete and/or inconsistent information.…”

Section: Our Contributionmentioning

confidence: 99%

EMIL: Extracting Meaning from Inconsistent Language

Straß

Wyner

Diller³

2019

International Journal of Approximate Reasoning

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There are well-developed formal and computational theories of argumentation to reason in the face of inconsistency, some with implementations; there are recent efforts to extract arguments from large textual corpora. Both developments are leading towards automated processing and reasoning with inconsistent, linguistically expressed knowledge in order to provide explanations and justifications in a form accessible to humans. However, there remains a gap between the knowledge-bases of computational theories of argumentation, which are generally coarse-grained and semi-structured (e.g. propositional logic), and inferences from knowledge-bases derived from natural language, which are fine-grained and highly structured (e.g. predicate logic). Arguments that occur in textual corpora are very rich, highly various, and incompletely understood. We identify several subproblems which must be addressed in order to bridge the gap, requiring the development of a computational foundation for argumentation coupled with natural language processing. For the computational foundation, we provide a direct semantics, a formal approach for argumentation, which is implemented and suitable to represent and reason with an associated natural language expression for defeasibility. It has attractive properties with respect to expressivity and complexity; we can reason by cases; we can structure higher level argumentation components such as cases and debates. With the implementation, we output experimental results which emphasise the importance of our efficient approach. To motivate our formal approach, we identify a range of issues found in other approaches. For the natural language processing, we adopt and adapt an existing controlled natural language (CNL) to interface with our computational theory of argumentation; the tool takes natural language input and automatically outputs expressions suitable for automated inference engines. A CNL, as a constrained fragment of natural language, helps to control variables, highlights key problems, and provides a framework to engineer solutions. The key adaptation incorporates the expression 'it is usual that', which is a plausibly 'natural' linguistic expression of defeasibility. This is an important, albeit incremental, step towards the incorporation of linguistic expressions of defeasibility; yet, by engineering such specific solutions, a range of other, relevant issues arise to be addressed. Overall, we can input arguments expressed in a controlled natural language, translate them to a formal knowledge base, represent the knowledge in a rule language, reason with the rules, generate argument extensions, and finally convert the arguments in the extensions into natural language. Our approach makes for fine-grained, highly structure, accessible, and linguistically represented argumentation evaluation. The overall novel contribution of the paper is an integrated, end-to-end argumentation system which bridges a gap between automated defeasible reasoning and a natural language interface. The component n...

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“…It is worth noticing that in the case of social networks where discussions tend to have more lengthy texts (like for example Reddit), we could consider the use of argumentation frameworks that use more structured arguments, like DeLP [29] or its more recent extension: the weighted argumentation framework RP-DeLP [1], that adds several defeasibility levels in the propositional logic knowledge base and it is based on a recursive ideal semantics. To transform the natural language texts to structured propositional logic knowledge bases, we could consider for example the recent approach followed in [52] to transform English natural language sentences to propositional logic sentences. However, with respect to Twitter, as we have commented above, the usually short length of tweets makes the extraction of more complex structures from single tweets unfeasible, so in the particular case of Twitter the approach of considering tweets as atomic arguments seems the most natural.…”

Section: Related Workmentioning

confidence: 99%

Weighted argumentation for analysis of discussions in Twitter

Alsinet

Argelich

Béjar

et al. 2017

International Journal of Approximate Reasoning

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Twitter has become a widely used social network to discuss ideas about many domains. This leads to a growing interest in understanding what are the major accepted or rejected opinions in different domains by social network users. At the same time, checking what are the topics that produce the most controversial discussions among users can be a good tool to discover topics that can be divisive, what can be useful, e.g., for policy makers. With the aim to automatically discover such information from Twitter discussions, we present an analysis system based on Valued Abstract Argumentation to model and reason about the accepted and rejected opinions. We consider different schemes to weight the opinions of Twitter users, such that we can tune the relevance of opinions considering different information sources from the social network. Towards having a fully automatic system, we also design a relation labeling system for discovering the relation between opinions. Regarding the underlying acceptability semantics, we use ideal semantics to compute accepted/rejected opinions. We define two measures over sets of accepted and rejected opinions to quantify the most controversial discussions. In order to validate our system, we analyze different real Twitter discussions from the political domain. The results show that different weighting schemes produce different sets of socially accepted opinions and that the controversy measures can reveal significant differences between discussions.

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Working on the argument pipeline: Through flow issues between natural language argument, instantiated arguments, and argumentation frameworks

Cited by 11 publications

References 35 publications

The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants

The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants

EMIL: Extracting Meaning from Inconsistent Language

Weighted argumentation for analysis of discussions in Twitter

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