Abstract
We show that text readability prediction improves significantly from hard parameter sharing with models predicting first pass duration, total fixation duration and regression duration. Specifically, we induce multi-task Multilayer Perceptrons and Logistic Regression models over sentence representations that capture various aggregate statistics, from two different text readability corpora for English, as well as the Dundee eye-tracking corpus. Our approach leads to significant improvements over Single task learning and over previous systems. In addition, our improvements are consistent across train sample sizes, making our approach especially applicable to small datasets.- Anthology ID:
- W17-5050
- Volume:
- Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Joel Tetreault, Jill Burstein, Claudia Leacock, Helen Yannakoudakis
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 438–443
- Language:
- URL:
- https://aclanthology.org/W17-5050
- DOI:
- 10.18653/v1/W17-5050
- Cite (ACL):
- Ana Valeria González-Garduño and Anders Søgaard. 2017. Using Gaze to Predict Text Readability. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 438–443, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Using Gaze to Predict Text Readability (González-Garduño & Søgaard, BEA 2017)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W17-5050.pdf