Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning

Botschen, Teresa; Sorokin, Daniil; Gurevych, Iryna

doi:10.18653/v1/w18-5211

Cited by 18 publications

(10 citation statements)

References 27 publications

(28 reference statements)

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“…The most successful previous work on ARCT (Choi and Lee, 2018;Zhao et al, 2018;Niven and Kao, 2018) involved transfer learning from Natural Language Inference (NLI) datasets (Bowman et al, 2015;Williams et al, 2017), and utilized effective NLI models such as ESIM (Chen et al, 2016) and InferSent (Conneau et al, 2017). More recently, Botschen et al (2018) added FrameNet knowledge with modest performance gains. These models should be evaluated on our adversarial dataset.…”

Section: Related Workmentioning

confidence: 99%

Probing Neural Network Comprehension of Natural Language Arguments

Niven¹,

Kao²

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

325

282

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We are surprised to find that BERT's peak performance of 77% on the Argument Reasoning Comprehension Task reaches just three points below the average untrained human baseline. However, we show that this result is entirely accounted for by exploitation of spurious statistical cues in the dataset. We analyze the nature of these cues and demonstrate that a range of models all exploit them. This analysis informs the construction of an adversarial dataset on which all models achieve random accuracy. Our adversarial dataset provides a more robust assessment of argument comprehension and should be adopted as the standard in future work. This is an updated version of our ACL paper.

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Section: Related Workmentioning

confidence: 99%

Probing Neural Network Comprehension of Natural Language Arguments

Niven¹,

Kao²

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

325

282

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show abstract

“…An approach to empower commonsense reasoning and make it more explainable with Argumentation is given in Botschen et al . (2018). The authors investigate whether external knowledge of event-based frames and fact-based entities can contribute to decompose an argument as stated in the Abstraction and Reasoning Corpus (ARC) task 12 .…”

Section: Argumentation and Explainable Systemsmentioning

confidence: 99%

Argumentation and explainable artificial intelligence: a survey

Vassiliades

Bassiliades

Patkos

2021

The Knowledge Engineering Review

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Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

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“…In contrast to our graph, the knowledge there concern the premises and conclusions in an argumentative discourse, and the goal is to identify the links between them. Botschen, Sorokin, and Gurevych (2018) used Wikidata as a knowledge base for argument reasoning comprehension, but conclude that world knowledge might not be sufficient and that a more logical analysis is needed. The knowledge graph we propose goes beyond simple world knowledge and has the potential to make reasoning easier.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Argumentation Knowledge Graph Construction

Alkhatib

Hou²,

Wachsmuth

et al. 2020

AAAI

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This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.

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Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning

Cited by 18 publications

References 27 publications

Probing Neural Network Comprehension of Natural Language Arguments

Probing Neural Network Comprehension of Natural Language Arguments

Argumentation and explainable artificial intelligence: a survey

End-to-End Argumentation Knowledge Graph Construction

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