Existing work on probing of pretrained language models (LMs) has predominantly focused on sentence-level syntactic tasks. In this paper, we introduce document-level discourse probing to evaluate the ability of pretrained LMs to capture document-level relations. We experiment with 7 pretrained LMs, 4 languages, and 7 discourse probing tasks, and find BART to be overall the best model at capturing discourse -- but only in its encoder, with BERT performing surprisingly well as the baseline model. Across the different models, there are substantial differences in which layers best capture discourse information, and large disparities between models.
@article{arxiv.2104.05882,
title = {Discourse Probing of Pretrained Language Models},
author = {Fajri Koto and Jey Han Lau and Timothy Baldwin},
journal= {arXiv preprint arXiv:2104.05882},
year = {2021}
}