SemEval-2020 Task 5: Counterfactual Recognition

Yang, Xiaoyu; Obadinma, Stephen; Zhao, Huasha; Zhang, Qiong; Matwin, Stan; Zhu, Xiaodan

doi:10.48550/arxiv.2008.00563

Cited by 2 publications

(4 citation statements)

References 29 publications

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“…FinCausal 2021 (Mariko et al, 2021), SemEval 2020 task 5 (Yang et al, 2020), and SemEval 2010 task 8 (Hendrickx et al, 2019). FinCausal 2021 is a causal news text extracted from the 2019 financial news corpus, most of which describes the financial situation in 2019.…”

Section: Datasetsmentioning

confidence: 99%

Edge‐featured graph attention network with dependency features for causality detection of events

et al. 2023

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Causality detection, as a more fine‐grained task than causality extraction, which aims to detect the components that represent the cause and effect in sentence‐level texts with causality, is a significant task in the field of Natural Language Processing (NLP). Previous research on causality detection has concentrated on text token features whilst ignoring the dependency attributes between tokens. In this paper, we propose a model that uses the Edge‐featured Graph Attention Network based on dependency‐directed graphs for the causality detection task. To begin, we convert the texts with causality into the representation of dependency‐directed graphs (DDG), which regard the dependency attributes between tokens as edge features. Then we use Edge‐featured Graph Attention Network to aggregate the node and edge features of DDG. Finally, we put the graph embedding into Bi‐directional Long Short‐Term Memory (BiLSTM) layer to learn the dependencies between forward and backward long‐distance nodes in DDG. Experiments on three datasets prove that this method achieves better performance in precision, recall, and other evaluation metrics compared with other methods.

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Section: Datasetsmentioning

confidence: 99%

Edge‐featured graph attention network with dependency features for causality detection of events

et al. 2023

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Section: Counterfactual Crementioning

confidence: 99%

“…There are a small number of papers that describe resources and techniques for detecting counterfactuals (Son et al 2017;Ojha et al 2020;Nwaike and Jiao 2020;Yang et al 2020b;Fajcik et al 2020). All but one paper found in the literature review is based around the SemEval-2020 Task 5 (Yang et al 2020b) which had two tasks. The first task was a classification task which identified if a sentence contained a counterfactual statement, and the second was an extraction task which identified the 'antecedent and consequent' (Yang et al 2020b) in a counterfactual.…”

Section: Counterfactual Crementioning

confidence: 99%

“…All but one paper found in the literature review is based around the SemEval-2020 Task 5 (Yang et al 2020b) which had two tasks. The first task was a classification task which identified if a sentence contained a counterfactual statement, and the second was an extraction task which identified the 'antecedent and consequent' (Yang et al 2020b) in a counterfactual. The technique proposed by Son et al (2017) used a combination of rules and a support vector machine (SVM) to capture sentences with counterfactual statements.…”

Section: Counterfactual Crementioning

confidence: 99%

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A survey of the extraction and applications of causal relations

Drury

Oliveira

Lopes³

2022

Nat. Lang. Eng.

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Causationin written natural language can express a strong relationship between events and facts. Causation in the written form can be referred to as a causal relation where a cause event entails the occurrence of an effect event. A cause and effect relationship is stronger than a correlation between events, and therefore aggregated causal relations extracted from large corpora can be used in numerous applications such as question-answering and summarisation to produce superior results than traditional approaches. Techniques like logical consequence allow causal relations to be used in niche practical applications such as event prediction which is useful for diverse domains such as security and finance. Until recently, the use of causal relations was a relatively unpopular technique because the causal relation extraction techniques were problematic, and the relations returned were incomplete, error prone or simplistic. The recent adoption of language models and improved relation extractors for natural language such as Transformer-XL (Dai et al. (2019). Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860) has seen a surge of research interest in the possibilities of using causal relations in practical applications. Until now, there has not been an extensive survey of the practical applications of causal relations; therefore, this survey is intended precisely to demonstrate the potential of causal relations. It is a comprehensive survey of the work on the extraction of causal relations and their applications, while also discussing the nature of causation and its representation in text.

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SemEval-2020 Task 5: Counterfactual Recognition

Cited by 2 publications

References 29 publications

Edge‐featured graph attention network with dependency features for causality detection of events

Edge‐featured graph attention network with dependency features for causality detection of events

A survey of the extraction and applications of causal relations

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