Intrinsic Probing Through Dimension Selection Paper Code

by Lucas Torroba Hennigen, Adina Williams, Ryan Cotterell

in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, November 2020

We introduce a novel framework for intrinsic probing that leverages a decomposable multivariate Gaussian probe. We run experiments on 36 languages from the Universal Dependencies treebanks, and find that fastText concentrates its linguistic structure more than BERT.

Machine Reading of Historical Events Paper Code

by Or Honovich*, Lucas Torroba Hennigen*, Omri Abend, Shay B. Cohen

in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, July 2020

We introduce the historical event ordering (HEO) task, where a series of short textual descriptions of historical events, potentially alongside some additional information, are ordered chronologically. We compile two datasets for this task, and compare the performance of two models in it.