NAACL 2021long96 citations
Understanding by Understanding Not: Modeling Negation in Language Models
Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, Aaron Courville
Abstract
Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language models often handle negation incorrectly. To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus. By training BERT with the resulting combined objective we reduce the mean top 1 error rate to 4% on the negated LAMA dataset. We also see some improvements on the negated NLI benchmarks.
BibTeX
@inproceedings{hosseini-etal-2021-understanding,
title = "Understanding by Understanding Not: Modeling Negation in Language Models",
author = "Hosseini, Arian and
Reddy, Siva and
Bahdanau, Dzmitry and
Hjelm, R Devon and
Sordoni, Alessandro and
Courville, Aaron",
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.102/",
doi = "10.18653/v1/2021.naacl-main.102",
pages = "1301--1312"
}