Towards Causal Explanation Detection with Pyramid Salient-Aware Network

Zuo, Xinyu; Chen, Yubo; Liu, Kang; Zhao, Jun

doi:10.1007/978-3-030-63031-7_9

“…This is a relatively small area of causality and therefore there were a limited number of papers discovered in the literature review. The dominant approach is machine learning (Son, Bayas, and Schwartz 2018;Zuo et al 2020b;Zuo et al 2020a). For example, Zuo et al (2020b) used pyramid salient-aware networks.…”

Section: Causal Explanation Detectionmentioning

confidence: 99%

“…The dominant approach is machine learning (Son, Bayas, and Schwartz 2018;Zuo et al 2020b;Zuo et al 2020a). For example, Zuo et al (2020b) used pyramid salient-aware networks. The technique identifies keywords in a discourse structure, and from these keywords identify the root or subject of the discourse.…”

Section: Causal Explanation Detectionmentioning

confidence: 99%

“…The aforementioned work used a self-attention network to identify the causal explanation. The technique score was 0.87, 0.77 and 0.82 on the Facebook, PDTB-CED and BECauSE-CED data sets, respectively (Zuo et al 2020b). The BECauSE-CED data and PDTB-CED sets are modified versions of the BECauSE (Dunietz et al 2017) and Penn Discourse TreeBank (PDTB) (Rashmi et al 2008) corpora.…”

Section: Causal Explanation Detectionmentioning

confidence: 99%

See 1 more Smart Citation

A survey of the extraction and applications of causal relations

Drury

¹

,

Oliveira

²

,

Lopes³

2022

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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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“…determining whether there is a causal relation between two events in a sentence. To this end, most existing methods adopt a supervised learning paradigm (Mirza and Tonelli, 2016;Riaz and Girju, 2014;Hashimoto et al, 2014;Hu and Walker, 2017;Gao et al, 2019;Zuo et al, 2020). Although these methods have achieved good performance, they usually need large-scale annotated training data.…”

Section: Introductionmentioning

confidence: 99%

KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision

Zuo¹,

Chen²,

Liu³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

Self Cite

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Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data.

show abstract

“…determining whether there is a causal relation between two events in a sentence. To this end, most existing methods adopt a supervised learning paradigm (Mirza and Tonelli, 2016;Riaz and Girju, 2014;Hashimoto et al, 2014;Hu and Walker, 2017;Gao et al, 2019;Zuo et al, 2020). Although these methods have achieved good performance, they usually need large-scale annotated training data.…”

Section: Introductionmentioning

confidence: 99%

KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision

Zuo

¹

,

Chen

²

,

Liu

³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data.

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Towards Causal Explanation Detection with Pyramid Salient-Aware Network

Cited by 7 publications

References 29 publications

A survey of the extraction and applications of causal relations

A survey of the extraction and applications of causal relations

KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision

KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision

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