# Measuring Contextual Informativeness in Child-Directed Text

arXiv: https://arxiv.org/abs/2412.17427 · HTML: https://mlsanity.com/paper/2412.17427/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity](https://mlsanity.com/paper/1608.00869/) (2016)
- [High-risk learning: acquiring new word vectors from tiny data](https://mlsanity.com/paper/1707.06556/) (2017)
- [Comparative Analysis of Word Embeddings for Capturing Word Similarities](https://mlsanity.com/paper/2005.03812/) (2020)
- [Learning Semantic Representations for Novel Words: Leveraging Both Form and Context](https://mlsanity.com/paper/1811.03866/) (2018)

## Built on by (most cited first, 3 of 3)

- [Tinker Tales: Interactive Storytelling Framework for Early Childhood Narrative Development and AI Literacy](https://mlsanity.com/paper/2504.13969/) (2025)
- [Factuality on Demand: Controlling the Factuality-Informativeness Trade-off in Text Generation](https://mlsanity.com/paper/2602.00848/) (2026)
- [Predicting Contextual Informativeness for Vocabulary Learning using Deep Learning](https://mlsanity.com/paper/2602.18326/) (2026)

## Abstract (arXiv)

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.