Finding Educationally Supportive Contexts for Vocabulary Learning with Attention-Based Models
Sungjin Nam, Kevyn Collins-Thompson, David Jurgens, Xin Tong
Abstract
When learning new vocabulary, both humans and machines acquire critical information about the meaning of an unfamiliar word through contextual information in a sentence or passage. However, not all contexts are equally helpful for learning an unfamiliar ‘target’ word. Some contexts provide a rich set of semantic clues to the target word’s meaning, while others are less supportive. We explore the task of finding educationally supportive contexts with respect to a given target word for vocabulary learning scenarios, particularly for improving student literacy skills. Because of their inherent context-based nature, attention-based deep learning methods provide an ideal starting point. We evaluate attention-based approaches for predicting the amount of educational support from contexts, ranging from a simple custom model using pre-trained embeddings with an additional attention layer, to a commercial Large Language Model (LLM). Using an existing major benchmark dataset for educational context support prediction, we found that a sophisticated but generic LLM had poor performance, while a simpler model using a custom attention-based approach achieved the best-known performance to date on this dataset.- Anthology ID:
- 2024.lrec-main.640
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 7286–7295
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.640
- DOI:
- Cite (ACL):
- Sungjin Nam, Kevyn Collins-Thompson, David Jurgens, and Xin Tong. 2024. Finding Educationally Supportive Contexts for Vocabulary Learning with Attention-Based Models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7286–7295, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Finding Educationally Supportive Contexts for Vocabulary Learning with Attention-Based Models (Nam et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2024.lrec-main.640.pdf