@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",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Measuring Contextual Informativeness in Child-Directed Text
%A Valentini, Maria R.
%A Wright, Téa Y.
%A Marashian, Ali
%A Ellis, Jennifer M.
%A Colunga, Eliana
%A von der Wense, Katharina
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F valentini-etal-2025-measuring
%X 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.
%U https://aclanthology.org/2025.coling-main.540/
%P 8109-8120
Markdown (Informal)
[Measuring Contextual Informativeness in Child-Directed Text](https://aclanthology.org/2025.coling-main.540/) (Valentini et al., COLING 2025)
ACL
- Maria R. Valentini, Téa Y. Wright, Ali Marashian, Jennifer M. Ellis, Eliana Colunga, and Katharina von der Wense. 2025. Measuring Contextual Informativeness in Child-Directed Text. In Proceedings of the 31st International Conference on Computational Linguistics, pages 8109–8120, Abu Dhabi, UAE. Association for Computational Linguistics.