@inproceedings{nam-etal-2024-finding,
    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.",
}

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.
Nam, Sungjin Collins-Thompson, Kevyn Jurgens, David Tong, Xin
Finding Educationally Supportive Contexts for Vocabulary Learning with Attention-Based Models