Using gaze to predict text readability

TY - GEN

T1 - Using gaze to predict text readability

AU - Gonzalez, Ana Valeria

AU - Søgaard, Anders

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

M3 - Article in proceedings

SN - 978-1-945626-85-2

SP - 438

EP - 443

BT - Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

PB - Association for Computational Linguistics

T2 - 12th Workshop on Innovative Use of NLP for Building Educational Applications

Y2 - 8 September 2017 through 8 September 2017

ER -