Scalar Adjective Identification and Multilingual Ranking
Aina Garí Soler, Marianna Apidianaki
Abstract
The intensity relationship that holds between scalar adjectives (e.g., nice < great < wonderful) is highly relevant for natural language inference and common-sense reasoning. Previous research on scalar adjective ranking has focused on English, mainly due to the availability of datasets for evaluation. We introduce a new multilingual dataset in order to promote research on scalar adjectives in new languages. We perform a series of experiments and set performance baselines on this dataset, using monolingual and multilingual contextual language models. Additionally, we introduce a new binary classification task for English scalar adjective identification which examines the models’ ability to distinguish scalar from relational adjectives. We probe contextualised representations and report baseline results for future comparison on this task.
BibTeX
@inproceedings{gari-soler-apidianaki-2021-scalar,
title = "Scalar Adjective Identification and Multilingual Ranking",
author = "Gar{\'i} Soler, Aina and
Apidianaki, Marianna",
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.370/",
doi = "10.18653/v1/2021.naacl-main.370",
pages = "4653--4660"
}