Answering Binary Causal Questions: A Transfer Learning Based Approach

Kayesh, Humayun; Islam, Md. Saiful; Wang, Junhu; Anirban, Shikha; Kayes, A. S. M.; Watters, Paul A.

doi:10.1109/ijcnn48605.2020.9207662

Cited by 6 publications

(14 citation statements)

References 15 publications

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“…Hassanzadeh et al evaluate the application of cause-effect pairs extracted from Gigawords corpus for binary question answering. Kayesh et al (2020) extends this work to automatically learn the yes/no threshold using word embeddings from BERT, RoBERTa, and other transformer-based models. Sharp et al and Xie and Mu (2019) Li et al (2021) present SCITE, a BiLSTM-CRF model which uses pretrained Flair embeddings and multi-headed self-attention to extract causal phrases.…”

Section: Related Workmentioning

confidence: 92%

Enhancing Multiple-Choice Question Answering with Causal Knowledge

Dalal¹,

Arčan²,

Buitelaar³

2021

Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Ar

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The task of causal question answering aims to reason about causes and effects over a provided real or hypothetical premise. Recent approaches have converged on using transformerbased language models to solve question answering tasks. However, pretrained language models often struggle when external knowledge is not present in the premise or when additional context is required to answer the question. To the best of our knowledge, no prior work has explored the efficacy of augmenting pretrained language models with external causal knowledge for multiple-choice causal question answering. In this paper, we present novel strategies for the representation of causal knowledge. Our empirical results demonstrate the efficacy of augmenting pretrained models with external causal knowledge. We show improved performance on the COPA (Choice of Plausible Alternatives) and WIQA (What If Reasoning Over Procedural Text) benchmark tasks. On the WIQA benchmark, our approach is competitive with the state-of-the-art and exceeds it within the evaluation subcategories of In-Paragraph and Out-of-Paragraph perturbations.

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Section: Related Workmentioning

confidence: 92%

Enhancing Multiple-Choice Question Answering with Causal Knowledge

Dalal¹,

Arčan²,

Buitelaar³

2021

Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Ar

View full text Add to dashboard Cite

show abstract

“…Therefore, the recent approaches apply supervised or weakly-supervised learning in causal discovery [14], [15]. Transfer learning-based approaches are also among the popular techniques for causality detection [1], [16], [2], [17].…”

Section: Related Workmentioning

confidence: 99%

“…Because of the syntactic diversity of concepts in natural languages, an exact match between knowledge base concepts and input concepts is not always available. The recent approaches to BCQs apply transfer learningbased models that are trained on automatically extracted causally-related concept pairs [1], [2]. Hassanzadeh et al [1] proposed an approach that uses bidirectional encoder representation from transformers [3] to encode a large training dataset and then applies a top-k nearest neighbour search technique to answer binary causal questions.…”

Section: Introductionmentioning

confidence: 99%

“…Hassanzadeh et al [1] proposed an approach that uses bidirectional encoder representation from transformers [3] to encode a large training dataset and then applies a top-k nearest neighbour search technique to answer binary causal questions. Later, we proposed a transfer learning-based approach [2] that was trained on a smaller dataset while keeping the performance comparable to the previous approach. However, both approaches suffer from low accuracy and precision scores.…”

Section: Introductionmentioning

confidence: 99%

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Answering Binary Causal Questions Using Role-Oriented Concept Embedding

Kayesh

Islam

Wang

2023

IEEE Trans. Artif. Intell.

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Answering binary causal questions is a challenging task, and it requires rich background knowledge to answer such questions. Extracting useful causal features from the background knowledge base and applying them effectively in a model is a crucial step to answering binary causal questions. The state-of-theart approaches apply deep learning techniques to answer binary causal questions. In these approaches, candidate concepts are often embedded into vectors to model causal relationships among them. However, a concept may play the role of a cause in one question, but it could be an effect in another question. This aspect has not been extensively explored in existing approaches. Roleoriented causal concept embeddings are proposed in this paper to model causality between concepts. We also propose leveraging semantic concept similarity to extract causal information from concepts. Finally, we develop a deep learning framework to answer binary causal questions. Our approach yields accuracy that is comparable to or better than the benchmark approaches.Impact Statement-Understanding causality is crucial for automatic question-answering systems, which are useful in extracting and distributing human knowledge. An automatic question-answering system with causal knowledge can be used to check whether there is causal relationship between two concepts.Existing approaches to answer binary causal questions often answer such questions with close to 55% accuracy due to the limited usage of causal and contextual features. The deep learning framework we propose in this paper uses a role-oriented concept embedding to address such issues. Our approach achieves better accuracy by up to 3.6%, compared to the state-of-the-art benchmark approaches. The proposed approach can be used in a variety of fields, including prescriptive analysis, event prediction, and any other area where entity relationships are essential. It could also be used to improve the retrieval of causality-related inquiries in web search engines.

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“…Other application areas of causality detection in social media include political events analysis 1 , income analysis 2 , career path prediction 3 , adverse drug reaction (ADR) detection 4 , and automatic question answering 5 . For instance, postmarketing surveillance of drugs is a vital activity of the drug safety authority.…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for mining and detecting causally related events in tweets

Kayesh

Islam

Wang

et al. 2020

Concurrency and Computation

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Nowadays, public gatherings and social events are an integral part of a modern city life. To run such events seamlessly, it requires real time mining and monitoring of causally related events so that the management can make informed decisions and take appropriate actions. The automatic detection of event causality from short text such as tweets could be useful for event management in this context. However, detecting event causality from tweets is a challenging task. Tweets are short, unstructured, and often written in highly informal language which lacks enough contextual information to detect causality. The existing approaches apply different techniques including hand‐crafted linguistic rules and machine learning models. However, none of the approaches tackle the issue related to the lack of contextual information. In this paper, we detect event causality in tweets by applying a context word extension technique and a deep causal event detection model. The context word extension technique is driven by background knowledge extracted from one million news articles. Our model achieves 79.35% recall and 67.28% f1‐score, which are 17.39% and 2.33% improvements to the state‐of‐the‐art approach.

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Answering Binary Causal Questions: A Transfer Learning Based Approach

Cited by 6 publications

References 15 publications

Enhancing Multiple-Choice Question Answering with Causal Knowledge

Enhancing Multiple-Choice Question Answering with Causal Knowledge

Answering Binary Causal Questions Using Role-Oriented Concept Embedding

A deep learning model for mining and detecting causally related events in tweets

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