Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases
Qing Lyu, Zheng Hua, Daoxin Li, Li Zhang, Marianna Apidianaki, Chris Callison-Burch
Abstract
Recursive noun phrases (NPs) have interesting semantic properties. For example, “my favorite new movie” is not necessarily my favorite movie, whereas “my new favorite movie” is. This is common sense to humans, yet it is unknown whether language models have such knowledge. We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs. When evaluated on RNPC, state-of-the-art Transformer models only perform around chance. Still, we show that such knowledge is learnable with appropriate data. We further probe the models for relevant linguistic features that can be learned from our tasks, including modifier semantic category and modifier scope. Finally, models trained on RNPC achieve strong zero-shot performance on an extrinsic Harm Detection evaluation task, showing the usefulness of the understanding of recursive NPs in downstream applications.
BibTeX
@inproceedings{lyu-etal-2022-favorite,
title = "Is {\textquotedblleft}My Favorite New Movie{\textquotedblright} My Favorite Movie? Probing the Understanding of Recursive Noun Phrases",
author = "Lyu, Qing and
Hua, Zheng and
Li, Daoxin and
Zhang, Li and
Apidianaki, Marianna and
Callison-Burch, Chris",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.388/",
doi = "10.18653/v1/2022.naacl-main.388",
pages = "5286--5302"
}