Measuring Contextual Informativeness in Child-Directed Text
Maria R. Valentini, Téa Y. Wright, Ali Marashian, Jennifer M. Ellis, Eliana Colunga, Katharina von der Wense
Abstract
To address an important gap in creating children’s stories for vocabulary enrichment, we investigate the automatic evaluation of how well stories convey the semantics of target vocabulary words, a task with substantial implications for generating educational content. We motivate this task, which we call measuring contextual informativeness in children’s stories, and provide a formal task definition as well as a dataset for the task. We further propose a method for automating the task using a large language model (LLM). Our experiments show that our approach reaches a Spearman correlation of 0.4983 with human judgments of informativeness, while the strongest baseline only obtains a correlation of 0.3534. An additional analysis shows that the LLM-based approach is able to generalize to measuring contextual informativeness in adult-directed text, on which it also outperforms all baselines.
BibTeX
@inproceedings{valentini-etal-2025-measuring,
title = "Measuring Contextual Informativeness in Child-Directed Text",
author = "Valentini, Maria R. and
Wright, T{\'e}a Y. and
Marashian, Ali and
Ellis, Jennifer M. and
Colunga, Eliana and
von der Wense, Katharina",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.540/",
pages = "8109--8120"
}