Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Colleges, School and Institutes

Abstract

In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children’s ability to understand others’ thoughts, feelings, and desires (or “mindreading”). We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy preserves the original rating. We also carry out multiple experiments to measure how much each augmentation strategy improves the performance of automatic scoring systems. To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children’s performance on tests of mindreading, consisting of 10,320 question-answer pairs. We obtain a new state-of-the-art performance on the MIND-CA corpus, improving macro-F1-score by 6 points. Results indicate that both the number of training examples and the quality of the augmentation strategies affect the performance of the systems. The task-specific augmentations generally outperform task-agnostic augmentations. Automatic augmentations based on vectors (GloVe, FastText) perform the worst. We find that systems trained on MIND-CA generalize well to UK-MIND-20. We demonstrate that data augmentation strategies also improve the performance on unseen data.

Details

Original languageEnglish
Title of host publicationProceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
EditorsChengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Publication statusPublished - 27 Jul 2021
EventThe Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing - Berkeley Hotel, Bangkok, Thailand
Duration: 1 Aug 20216 Aug 2021

Publication series

NameInternational Joint Conference on Natural Language Processing (IJCNLP)

Conference

ConferenceThe Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing
Abbreviated titleACL-IJCNLP 2021
Country/TerritoryThailand
CityBangkok
Period1/08/216/08/21