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.
REF show that text readability prediction improves significantly from hard parameter sharing when models try to predict word-based gaze features in a multi-task-learning setup.
Using Gaze to Predict Text Readability
BEA@EMNLP
Computer Science