Transfer Learning for Causal Sentence Detection

Kyriakakis, Manolis; Androutsopoulos, Ion; Saudabayev, Artur; Ametllé, Joan Ginés i

doi:10.18653/v1/w19-5031

Cited by 25 publications

(17 citation statements)

References 26 publications

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“…The studies of [51,56,89,91,98] employ different deep learning models in order to extract causality from the SemEval-2010 Task 8 dataset. Xu et al [91] use LSTM to learn higherlevel semantic and syntactic representations along the shortest dependency path (SDP), while Li et al [56] combine BiLSTM with multi-head self-attention (MHSA) to direct attention to long-range dependencies between words.…”

Section: Explicit Intra-sentential Causalitymentioning

confidence: 99%

“…This is the main reason for the F-score of [98] to be lower than [89] by 3.2%. Kyriakakis et al [51] explore the application of PTMs like BERT and ELMO [72] in the context of CE, by using bidirectional GRU with self-attention (BIGRUATT) as the baseline. The experimental results show that PTMs are only helpful for datasets with hundreds of training examples, and that BIGRUATT reaches a performance plateau when thousands of training instances are available.…”

Section: Explicit Intra-sentential Causalitymentioning

confidence: 99%

“…In intra-sentential causality, the "cause" and the "effect" lie in a single sentence, while in inter-sentential causality, the "cause" and the "effect" lie in different sentences. Most CE approaches, like [25,26,47,51,63,79], identify causality in the basic levels, which are explicit and/or intra-sentential forms. As There was no debate as the Senate passed the bill on to the House [10] Causal As It has a fixed time, as collectors well know [10] Non-causal After Bischoff in a round table discussion claimed he fired Austin after he refused to do a taping in Atlanta [61] Causal After In stark contrast to his predecessor, five days after his election he spoke of his determination to do what he could to bring peace [61] Non-causal -He derives great joy and happiness from cycling [5] C a u s a l However, causality in many texts is implicit and/or inter-sentential conditions, which are more complicated than basic kinds of causality.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A survey on extraction of causal relations from natural language text

Han

Poon

2022

Knowl Inf Syst

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As an essential component of human cognition, cause–effect relations appear frequently in text, and curating cause–effect relations from text helps in building causal networks for predictive tasks. Existing causality extraction techniques include knowledge-based, statistical machine learning (ML)-based, and deep learning-based approaches. Each method has its advantages and weaknesses. For example, knowledge-based methods are understandable but require extensive manual domain knowledge and have poor cross-domain applicability. Statistical machine learning methods are more automated because of natural language processing (NLP) toolkits. However, feature engineering is labor-intensive, and toolkits may lead to error propagation. In the past few years, deep learning techniques attract substantial attention from NLP researchers because of its powerful representation learning ability and the rapid increase in computational resources. Their limitations include high computational costs and a lack of adequate annotated training data. In this paper, we conduct a comprehensive survey of causality extraction. We initially introduce primary forms existing in the causality extraction: explicit intra-sentential causality, implicit causality, and inter-sentential causality. Next, we list benchmark datasets and modeling assessment methods for causal relation extraction. Then, we present a structured overview of the three techniques with their representative systems. Lastly, we highlight existing open challenges with their potential directions.

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Section: Explicit Intra-sentential Causalitymentioning

confidence: 99%

Section: Explicit Intra-sentential Causalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A survey on extraction of causal relations from natural language text

Han

Poon

2022

Knowl Inf Syst

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“…4. Opposed to pattern-matching are feature-based classification methods: recent papers apply neural networks and exploit-similarly to our approach-the Transfer Learning capability of BERT [31]. However, we see a number of problems with these papers regarding the realization of our described RE use cases: First, neither the code nor a demonstration is published, making it difficult to reproduce the results and test the performance on data from the RE domain.…”

Section: Causality In Requirements Engineeringmentioning

confidence: 99%

Causality in requirements artifacts: prevalence, detection, and impact

et al. 2022

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Causal relations in natural language (NL) requirements convey strong, semantic information. Automatically extracting such causal information enables multiple use cases, such as test case generation, but it also requires to reliably detect causal relations in the first place. Currently, this is still a cumbersome task as causality in NL requirements is still barely understood and, thus, barely detectable. In our empirically informed research, we aim at better understanding the notion of causality and supporting the automatic extraction of causal relations in NL requirements. In a first case study, we investigate 14.983 sentences from 53 requirements documents to understand the extent and form in which causality occurs. Second, we present and evaluate a tool-supported approach, called CiRA, for causality detection. We conclude with a second case study where we demonstrate the applicability of our tool and investigate the impact of causality on NL requirements. The first case study shows that causality constitutes around 28 % of all NL requirements sentences. We then demonstrate that our detection tool achieves a macro-$$\hbox {F}_{1}$$ F 1 score of 82 % on real-world data and that it outperforms related approaches with an average gain of 11.06 % in macro-Recall and 11.43 % in macro-Precision. Finally, our second case study corroborates the positive correlations of causality with features of NL requirements. The results strengthen our confidence in the eligibility of causal relations for downstream reuse, while our tool and publicly available data constitute a first step in the ongoing endeavors of utilizing causality in RE and beyond.

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“…Hence, we believe systems to detect and parse causal relationships would perform well on this task as well. With respect to the first subtask, fine-tuning neural models using information-rich word embeddings seems to form the state-of-the-art (Kyriakakis et al, 2019) and forms the backbone of our submitted approach as well. The second subtask bears a resemblance to relation-entity extraction (of which cause-effect may be considered a specific case), which has also been recently dominated by neural models (Li and Tian, 2020;Soares et al, 2019).…”

Section: Previous and Related Workmentioning

confidence: 99%

IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals

Ojha¹,

Garg²,

Gupta

et al. 2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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This paper describes our efforts in tackling Task 5 (Yang et al., 2020) of SemEval-2020. The task involved detecting a class of textual expressions known as counterfactuals and separating them into their constituent elements. Counterfactual statements describe events that have not or could not have occurred and the possible implications of such events. While counterfactual reasoning is natural for humans, understanding these expressions is difficult for artificial agents due to a variety of linguistic subtleties. Our final submitted approaches were an ensemble of various fine-tuned transformer-based and CNN-based models for the first subtask and a transformer model with dependency tree information for the second subtask. We ranked 4 th and 9 th in the overall leaderboard. We also explored various other approaches that involved the use of classical methods, other neural architectures and the incorporation of different linguistic features.

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Transfer Learning for Causal Sentence Detection

Cited by 25 publications

References 26 publications

A survey on extraction of causal relations from natural language text

A survey on extraction of causal relations from natural language text

Causality in requirements artifacts: prevalence, detection, and impact

IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals

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