The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing

Valentina Pyatkin, Shoval Sadde, Aynat Rubinstein, Paul Portner, Reut Tsarfaty


Abstract
Modality is the linguistic ability to describe vents with added information such as how desirable, plausible, or feasible they are. Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and more. Previous studies that address modality detection in NLP often restrict modal expressions to a closed syntactic class, and the modal sense labels are vastly different across different studies, lacking an accepted standard. Furthermore, these senses are often analyzed independently of the events that they modify. This work builds on the theoretical foundations of the Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to propose an event-based modality detection task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. We present experiments on the GME corpus aiming to detect and classify fine-grained modal concepts and associate them with their modified events. We show that detecting and classifying modal expressions is not only feasible, it also improves the detection of modal events in their own right.
Anthology ID:
2021.acl-long.77
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
953–965
Language:
URL:
https://aclanthology.org/2021.acl-long.77
DOI:
10.18653/v1/2021.acl-long.77
Bibkey:
Cite (ACL):
Valentina Pyatkin, Shoval Sadde, Aynat Rubinstein, Paul Portner, and Reut Tsarfaty. 2021. The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 953–965, Online. Association for Computational Linguistics.
Cite (Informal):
The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing (Pyatkin et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.77.pdf
Video:
 https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.77.mp4
Code
 OnlpLab/Modality-Corpus +  additional community code