Abstract
This article studies the relationship between text readability indice and automatic machine understanding systems. Our hypothesis is that the simpler a text is, the better it should be understood by a machine. We thus expect to a strong correlation between readability levels on the one hand, and performance of automatic reading systems on the other hand. We test this hypothesis with several understanding systems based on language models of varying strengths, measuring this correlation on two corpora of journalistic texts. Our results suggest that this correlation is rather small that existing comprehension systems are far to reproduce the gradual improvement of their performance on texts of decreasing complexity.- Anthology ID:
- W19-4443
- Volume:
- Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Helen Yannakoudakis, Ekaterina Kochmar, Claudia Leacock, Nitin Madnani, Ildikó Pilán, Torsten Zesch
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 412–422
- Language:
- URL:
- https://aclanthology.org/W19-4443
- DOI:
- 10.18653/v1/W19-4443
- Cite (ACL):
- Marc Benzahra and François Yvon. 2019. Measuring text readability with machine comprehension: a pilot study. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 412–422, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Measuring text readability with machine comprehension: a pilot study (Benzahra & Yvon, BEA 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W19-4443.pdf
- Data
- WebText