Answering Binary Causal Questions: A Transfer Learning Based Approach

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Kayesh, Humayun
Islam, Md Saiful
Wang, Junhu
Anirban, Shikha
Kayes, ASM
Watters, Paul
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2020
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Abstract

Causal question answering is a task of answering causality related questions. The questions are referred to as binary causal questions when the questions e.g., "Could X cause Y?" can be answered by yes/no answers. Answer to the previous question is yes if X is a cause of Y, and otherwise no. The binary causal question answering systems can be used to validate causal relationships, which can be particularly useful for decision making. For example, it could be useful for the tourism authorities to know the answer to the question "Could growing social tension cause reduction in tourism?". We aim to automatically answer such binary causal questions by developing a machine learning model. However, training a machine learning model to detect causal relationships is challenging due to the lack of large and high quality labeled datasets. In this paper, we propose a transfer learning-based approach which fine-tunes pretrained transformer based language models on a small dataset of cause-effect pairs to detect causality and answer binary causal questions. The proposed approach achieves performance comparable to a number of benchmark approaches on five benchmark test datasets extracted by human experts conditioned on the same small training dataset.

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2020 International Joint Conference on Neural Networks (IJCNN 2020)
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© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Natural language processing
Deep learning
Artificial intelligence not elsewhere classified
Knowledge and information management
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Kayesh, H; Islam, MS; Wang, J; Anirban, S; Kayes, ASM; Watters, P, Answering Binary Causal Questions: A Transfer Learning Based Approach, 2020 International Joint Conference on Neural Networks (IJCNN 2020), 2020