@inproceedings{nam-etal-2024-finding-educationally,
title = "Finding Educationally Supportive Contexts for Vocabulary Learning with Attention-Based Models",
author = "Nam, Sungjin and
Collins-Thompson, Kevyn and
Jurgens, David and
Tong, Xin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.640",
pages = "7286--7295",
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.",
}
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%0 Conference Proceedings
%T Finding Educationally Supportive Contexts for Vocabulary Learning with Attention-Based Models
%A Nam, Sungjin
%A Collins-Thompson, Kevyn
%A Jurgens, David
%A Tong, Xin
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F nam-etal-2024-finding-educationally
%X 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.
%U https://aclanthology.org/2024.lrec-main.640
%P 7286-7295
Markdown (Informal)
[Finding Educationally Supportive Contexts for Vocabulary Learning with Attention-Based Models](https://aclanthology.org/2024.lrec-main.640) (Nam et al., LREC-COLING 2024)
ACL