ACL 2021short6 citations
Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction
Ming Shen, Pratyay Banerjee, Chitta Baral
Abstract
In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting. Firstly, We evaluate our pre-trained model on various pronoun resolution datasets without any finetuning. Our method outperforms all previous unsupervised methods on all datasets by large margins. Secondly, we proceed to a few-shot setting where we finetune our pre-trained model on WinoGrande-S and XS separately. Our method outperforms RoBERTa-large baseline with large margins, meanwhile, achieving a higher AUC score after further finetuning on the remaining three official splits of WinoGrande.
BibTeX
@inproceedings{shen-etal-2021-unsupervised,
title = "Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction",
author = "Shen, Ming and
Banerjee, Pratyay and
Baral, Chitta",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.117/",
doi = "10.18653/v1/2021.acl-short.117",
pages = "932--941"
}