Team ReadMe at CMCL 2021 Shared Task: Predicting Human Reading Patterns by Traditional Oculomotor Control Models and Machine Learning

Alisan Balkoca, Abdullah Algan, Cengiz Acarturk, Çağrı Çöltekin


Abstract
This system description paper describes our participation in CMCL 2021 shared task on predicting human reading patterns. Our focus in this study is making use of well-known,traditional oculomotor control models and machine learning systems. We present experiments with a traditional oculomotor control model (the EZ Reader) and two machine learning models (a linear regression model and a re-current network model), as well as combining the two different models. In all experiments we test effects of features well-known in the literature for predicting reading patterns, such as frequency, word length and predictability.Our experiments support the earlier findings that such features are useful when combined.Furthermore, we show that although machine learning models perform better in comparison to traditional models, combination of both gives a consistent improvement for predicting multiple eye tracking variables during reading.
Anthology ID:
2021.cmcl-1.17
Volume:
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Month:
June
Year:
2021
Address:
Online
Venues:
CMCL | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
134–140
Language:
URL:
https://aclanthology.org/2021.cmcl-1.17
DOI:
10.18653/v1/2021.cmcl-1.17
Bibkey:
Cite (ACL):
Alisan Balkoca, Abdullah Algan, Cengiz Acarturk, and Çağrı Çöltekin. 2021. Team ReadMe at CMCL 2021 Shared Task: Predicting Human Reading Patterns by Traditional Oculomotor Control Models and Machine Learning. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 134–140, Online. Association for Computational Linguistics.
Cite (Informal):
Team ReadMe at CMCL 2021 Shared Task: Predicting Human Reading Patterns by Traditional Oculomotor Control Models and Machine Learning (Balkoca et al., CMCL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.cmcl-1.17.pdf