Age Recommendation from Texts and Sentences for Children

TL;DR

This study develops and evaluates machine learning models, including Transformers, to predict appropriate age levels for texts and sentences for children, achieving results comparable or superior to expert recommendations.

Contribution

It introduces a regression-based approach for age recommendation using Transformer models and analyzes linguistic features for explainability.

Findings

Transformer models outperform traditional models in age prediction.

Sentence-level predictions are comparable to expert assessments.

Text-level predictions outperform expert recommendations.

Abstract

Children have less text understanding capability than adults. Moreover, this capability differs among the children of different ages. Hence, automatically predicting a recommended age based on texts or sentences would be a great benefit to propose adequate texts to children and to help authors writing in the most appropriate way. This paper presents our recent advances on the age recommendation task. We consider age recommendation as a regression task, and discuss the need for appropriate evaluation metrics, study the use of state-of-the-art machine learning model, namely Transformers, and compare it to different models coming from the literature. Our results are also compared with recommendations made by experts. Further, this paper deals with preliminary explainability of the age prediction model by analyzing various linguistic features. We conduct the experiments on a dataset of 3, 673 French texts (132K sentences, 2.5M words). To recommend age at the text level and sentence level, our best models achieve MAE scores of 0.98 and 1.83 respectively on the test set. Also, compared to the recommendations made by experts, our sentence-level recommendation model gets a similar score to the experts, while the text-level recommendation model outperforms the experts by an MAE score of 1.48.