Abstract
Evaluation of text difficulty is important both for downstream tasks like text simplification, and for supporting educators in classrooms. Existing work on automated text complexity analysis uses linear models with engineered knowledge-driven features as inputs. While this offers interpretability, these models have lower accuracy for shorter texts. Traditional readability metrics have the additional drawback of not generalizing to informational texts such as science. We propose a neural approach, training on science and other informational texts, to mitigate both problems. Our results show that neural methods outperform knowledge-based linear models for short texts, and have the capacity to generalize to genres not present in the training data.- Anthology ID:
- W18-0505
- Volume:
- Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Joel Tetreault, Jill Burstein, Ekaterina Kochmar, Claudia Leacock, Helen Yannakoudakis
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 45–55
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/W18-0505/
- DOI:
- 10.18653/v1/W18-0505
- Cite (ACL):
- Farah Nadeem and Mari Ostendorf. 2018. Estimating Linguistic Complexity for Science Texts. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 45–55, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Estimating Linguistic Complexity for Science Texts (Nadeem & Ostendorf, BEA 2018)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/W18-0505.pdf
- Code
- Farahn/Liguistic-Complexity