Multilingual Language Models Predict Human Reading Behavior
Nora Hollenstein, Federico Pirovano, Ce Zhang, Lena Jäger, Lisa Beinborn
Abstract
We analyze if large language models are able to predict patterns of human reading behavior. We compare the performance of language-specific and multilingual pretrained transformer models to predict reading time measures reflecting natural human sentence processing on Dutch, English, German, and Russian texts. This results in accurate models of human reading behavior, which indicates that transformer models implicitly encode relative importance in language in a way that is comparable to human processing mechanisms. We find that BERT and XLM models successfully predict a range of eye tracking features. In a series of experiments, we analyze the cross-domain and cross-language abilities of these models and show how they reflect human sentence processing.
BibTeX
@inproceedings{hollenstein-etal-2021-multilingual,
title = "Multilingual Language Models Predict Human Reading Behavior",
author = {Hollenstein, Nora and
Pirovano, Federico and
Zhang, Ce and
J{\"a}ger, Lena and
Beinborn, Lisa},
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.10/",
doi = "10.18653/v1/2021.naacl-main.10",
pages = "106--123"
}