"What is on your mind?" Automated Scoring of Mindreading in Childhood and Early Adolescence
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Abstract (arXiv)
In this paper we present the first work on the automated scoring of mindreading ability in middle childhood and early adolescence. We create MIND-CA, a new corpus of 11,311 question-answer pairs in English from 1,066 children aged 7 to 14. We perform machine learning experiments and carry out extensive quantitative and qualitative evaluation. We obtain promising results, demonstrating the applicability of state-of-the-art NLP solutions to a new domain and task.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Nov 2020.
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.
- 2022longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attackscited by 8
- 2021Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading abilitycited by 2
- 2022longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks29 Jun 2022
- 2021Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading ability3 Jun 2021