ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment

Tarek Naous, Michael J Ryan, Anton Lavrouk, Mohit Chandra, Wei Xu


Abstract
We present a comprehensive evaluation of large language models for multilingual readability assessment. Existing evaluation resources lack domain and language diversity, limiting the ability for cross-domain and cross-lingual analyses. This paper introduces ReadMe++, a multilingual multi-domain dataset with human annotations of 9757 sentences in Arabic, English, French, Hindi, and Russian, collected from 112 different data sources. This benchmark will encourage research on developing robust multilingual readability assessment methods. Using ReadMe++, we benchmark multilingual and monolingual language models in the supervised, unsupervised, and few-shot prompting settings. The domain and language diversity in ReadMe++ enable us to test more effective few-shot prompting, and identify shortcomings in state-of-the-art unsupervised methods. Our experiments also reveal exciting results of superior domain generalization and enhanced cross-lingual transfer capabilities by models trained on ReadMe++. We will make our data publicly available and release a python package tool for multilingual sentence readability prediction using our trained models at: https://github.com/tareknaous/readme
Anthology ID:
2024.emnlp-main.682
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12230–12266
Language:
URL:
https://aclanthology.org/2024.emnlp-main.682
DOI:
10.18653/v1/2024.emnlp-main.682
Bibkey:
Cite (ACL):
Tarek Naous, Michael J Ryan, Anton Lavrouk, Mohit Chandra, and Wei Xu. 2024. ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 12230–12266, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment (Naous et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-main.682.pdf