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    <abstract>Recently, much research in psychology has benefited from the advances in machine learning techniques. Some recent studies showed that it is possible to build automated scoring models for children’s mindreading. These models were trained on a set of manually-labeled question-response pairs, which were collected by asking children to answer one or two questions after a short story is told or a video clip is played. However, existing models did not take the features of the stories and video clips into account when scoring, which obviously will reduce the accuracy of the scoring models. Furthermore, considering that different psychological tests may contain the same questions, this approach cannot be extended to other related psychological test datasets. In this study, we proposed a multi-modal learning framework to leverage the features extracted from the stories and videos related to the questions being asked during the children’s mindreading evaluation. Experimental results show that the scores produced by the proposed models agree well with those graded by human experts, highlighting the potential of the proposed network architecture for practical automated children’s mindreading scoring systems.</abstract>
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%0 Conference Proceedings
%T Measure Children’s Mindreading Ability with Machine Reading
%A Yan, Yuliang
%A Wang, Xiaohua
%A Zhou, Xiang
%A Zheng, Xiaoqing
%A Huang, Xuanjing
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yan-etal-2023-measure
%X Recently, much research in psychology has benefited from the advances in machine learning techniques. Some recent studies showed that it is possible to build automated scoring models for children’s mindreading. These models were trained on a set of manually-labeled question-response pairs, which were collected by asking children to answer one or two questions after a short story is told or a video clip is played. However, existing models did not take the features of the stories and video clips into account when scoring, which obviously will reduce the accuracy of the scoring models. Furthermore, considering that different psychological tests may contain the same questions, this approach cannot be extended to other related psychological test datasets. In this study, we proposed a multi-modal learning framework to leverage the features extracted from the stories and videos related to the questions being asked during the children’s mindreading evaluation. Experimental results show that the scores produced by the proposed models agree well with those graded by human experts, highlighting the potential of the proposed network architecture for practical automated children’s mindreading scoring systems.
%R 10.18653/v1/2023.findings-emnlp.561
%U https://aclanthology.org/2023.findings-emnlp.561
%U https://doi.org/10.18653/v1/2023.findings-emnlp.561
%P 8366-8375

Markdown (Informal)

[Measure Children’s Mindreading Ability with Machine Reading](https://aclanthology.org/2023.findings-emnlp.561) (Yan et al., Findings 2023)

• Measure Children’s Mindreading Ability with Machine Reading (Yan et al., Findings 2023)

ACL

• Yuliang Yan, Xiaohua Wang, Xiang Zhou, Xiaoqing Zheng, and Xuanjing Huang. 2023. Measure Children’s Mindreading Ability with Machine Reading. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8366–8375, Singapore. Association for Computational Linguistics.