Year: 2025
Type: conference-paper
Abstract: Knowing which words language learners struggle with is crucial for developing personalised education technologies.In this paper, we advocate for the novel task of "dictionary look-up prediction" as a means for evaluating the complexity of words in reading tasks.We release the Dictionary Look-Up development dataset (DLU-dev) and the Dialogue Dictionary Look-Up dataset (D-DLU), which is based on chatbot dialogues.We demonstrate that dictionary look-up is a challenging task for LLMs (results are presented for LLaMA, Gemma, and Longformer models).We explore finetuning with the ROC* loss function as a more appropriate loss for this task than the commonly used Binary Cross Entropy (BCE).We show that a feature-based model outperforms the LLMs.Finally, we investigate the transfer between DLU and the related tasks of Complex Word Identification (CWI) and Semantic Error Prediction (SEP), establishing new state-of-the-art results for SEP.
Institutions: Cambridge University Press, University of Sheffield
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Funder: University of Cambridge
OpenAlex ID: W4412888975