Answering binary causal questions: a transfer learning based approach

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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN)
Subtitle of host publication2020 conference proceedings
Place of PublicationPiscataway, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)9781728169262
Publication statusPublished - Jul 2020
Externally publishedYes
Event2020 International Joint Conference on Neural Networks - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020


Conference2020 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow


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