Abstract
Deep Learning (DL) techniques have been increasingly adopted for Automatic Text Scoring in education. However, these techniques often suffer from their inabilities to explain and justify how a prediction is made, which, unavoidably, decreases their trustworthiness and hinders educators from embracing them in practice. This study aimed to investigate whether (and to what extent) DL-based graders align with human graders regarding the important words they identify when marking short answer questions. To this end, we first conducted a user study to ask human graders to manually annotate important words in assessing answer quality and then measured the overlap between these human-annotated words and those identified by DL-based graders (i.e., those receiving large attention weights). Furthermore, we ran a randomized controlled experiment to explore the impact of highlighting important words detected by DL-based graders on human grading. The results showed that: (i) DL-based graders, to a certain degree, displayed alignment with human graders no matter whether DL-based graders and human graders agreed on the quality of an answer; and (ii) it is possible to facilitate human grading by highlighting those DL-detected important words, though further investigations are necessary to understand how human graders exploit such highlighted words.- Anthology ID:
- 2022.naacl-main.14
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 191–205
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.14
- DOI:
- 10.18653/v1/2022.naacl-main.14
- Cite (ACL):
- Zijie Zeng, Xinyu Li, Dragan Gasevic, and Guanliang Chen. 2022. Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring?. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 191–205, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring? (Zeng et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.naacl-main.14.pdf