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

Naous T, Ryan MJ, Lavrouk A, Chandra M, Xu W

Open source

DOI
10.18653/v1/2024.emnlp-main.682
Published
2024 Nov
Container
Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
Publisher
Not recorded
Open access
yes

Credibility signals

limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.18653/v1/2024.emnlp-main.682,
  title = {ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment.},
  author = {Naous T and Ryan MJ and Lavrouk A and Chandra M and Xu W},
  year = {2024},
  journal = {Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing},
  doi = {10.18653/v1/2024.emnlp-main.682},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.682}
}

RIS

TY  - JOUR
TI  - ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment.
AU  - Naous T
AU  - Ryan MJ
AU  - Lavrouk A
AU  - Chandra M
AU  - Xu W
PY  - 2024
JO  - Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
DO  - 10.18653/v1/2024.emnlp-main.682
UR  - https://doi.org/10.18653/v1/2024.emnlp-main.682
ER  - 

APA

T, N., MJ, R., A, L., M, C., & W, X. (2024). ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment.. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2024.emnlp-main.682

Source records