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

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

doi:10.18653/v1/2020.coling-main.135

Cited by 40 publications

(33 citation statements)

References 21 publications

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“…Caselli and Vossen (2017) (Chaudhary, 2020). Zuo et al (2020) solved the data lacking problem of ECI with the distantly supervised labeled training data. However, including the distant supervision, most of the existing data augmentation methods for NLP tasks are task-independent frameworks (Related work of data augmentation and dual learning are detailed in Appendix B).…”

Section: Related Workmentioning

confidence: 99%

LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification

Zuo¹,

Cao²,

Chen³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLPrelated augmentation methods cannot directly produce available data required for this task. To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework. On the one hand, our approach is knowledge guided, which can leverage existing knowledge bases to generate well-formed new sentences. On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework, and can interactively adjust the generation process to generate task-related sentences. Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show that 1) our method can augment suitable task-related training data for ECI; 2) our method outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.5 and +2.1 points on F1 value respectively).

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Section: Related Workmentioning

confidence: 99%

LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification

Zuo¹,

Cao²,

Chen³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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“…propose a mention masking generalization method and also consider the external structural knowledge. The very recent work (Zuo et al, 2020) propose a data augmentation method to alleviate the data lacking problem for the task. Regarding datasets construction, Mirza (2014) annotates the Causal-TimeBank dataset about event causal relations in the TempEval-3 corpus.…”

Section: Related Workmentioning

confidence: 99%

“…Our context encoder is based on the Transformer architecture (Vaswani et al, 2017). We adopt the BERT (Devlin et al, 2019) to encode the input sentence, 2 which has achieved the state-of-the-art performance for ECI task Zuo et al, 2020). After using BERT encoder to compute the contextual representations of the entire sentence, we concatenate representations of [CLS], e 1 and e 2 as the context representation regarding to the event pair (e 1 , e 2 ), namely…”

Section: Context Encodingmentioning

confidence: 99%

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Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks

Cao¹,

Zuo²,

Chen³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Identifying causal relations of events is an important task in natural language processing area. However, the task is very challenging, because event causality is usually expressed in diverse forms that often lack explicit causal clues. Existing methods cannot handle well the problem, especially in the condition of lacking training data. Nonetheless, humans can make a correct judgement based on their background knowledge, including descriptive knowledge and relational knowledge. Inspired by it, we propose a novel Latent Structure Induction Network (LSIN) to incorporate the external structural knowledge into this task. Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge. To leverage the relational knowledge, we propose a Relational Graph Induction module which is able to automatically learn a reasoning structure for event causality reasoning. Experimental results on two widely used datasets indicate that our approach significantly outperforms previous state-of-the-art methods.

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“…It will still suffer from data limitations and have no capacity to handling unseen contexts. Moreover, Zuo et al (2020) improved the performance of ECI with the distantly supervised labeled training data. However, their models are still limited to the unsatisfied qualities of the automatically generated data.…”

Section: Billy Finds His Childhood Teddy Bear >Causes/enables>mentioning

confidence: 99%

Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement

Zuo¹,

Cao²,

Chen³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training. Unfortunately, the scale of current annotated datasets is relatively limited, which cannot provide sufficient support for models to capture useful indicators from causal statements, especially for handing those new, unseen cases. To alleviate this problem, we propose a novel approach, shortly named CauSeRL, which leverages external causal statements for event causality identification. First of all, we design a self-supervised framework to learn context-specific causal patterns from external causal statements. Then, we adopt a contrastive transfer strategy to incorporate the learned context-specific causal patterns into the target ECI model. Experimental results show that our method significantly outperforms previous methods on EventSto-ryLine and Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively).

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

Cited by 40 publications

References 21 publications

LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification

LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification

Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks

Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement

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