QA-Adj: Adding Adjectives to QA-based Semantics

Leon Pesahov, Ayal Klein, Ido Dagan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Identifying all predicate-argument relations in a sentence has been a fundamental research target in NLP. While traditionally these relations were modeled via formal schemata, the recent QA-SRL paradigm (and its extensions) present appealing advantages of capturing such relations through intuitive natural language question-answer (QA) pairs. In this paper, we extend the QA-based semantics framework to cover adjectival predicates, which carry important information in many downstream settings yet have been scarcely addressed in NLP research. Firstly, based on some prior literature and empirical assessment, we propose capturing four types of core adjectival arguments, through corresponding question types. Notably, our coverage goes beyond prior annotations of adjectival arguments, while also explicating valuable implicit arguments. Next, we develop an extensive data annotation methodology, involving controlled crowdsourcing and targeted expert review. Following, we create a high-quality dataset, consisting of 9K adjective mentions with 12K predicate-argument instances (QAs). Finally, we present and analyze baseline models based on text-to-text language modeling, indicating challenges for future research, particularly regarding the scarce argument types. Overall, we suggest that our contributions can provide the basis for research on contemporary modeling of adjectival information.

Original languageEnglish
Title of host publicationDMR 2023 - 4th International Workshop on Designing Meaning Representations, Proceedings of the Workshop
EditorsJulia Bonn, Nianwen Xue
PublisherAssociation for Computational Linguistics (ACL)
Number of pages15
ISBN (Electronic)9781959429654
StatePublished - 2023
Event4th InternationalWorkshop on Designing Meaning Representations, DMR 2023 - Nancy, France
Duration: 20 Jun 202323 Jun 2023

Publication series

NameDMR 2023 - 4th International Workshop on Designing Meaning Representations, Proceedings of the Workshop


Conference4th InternationalWorkshop on Designing Meaning Representations, DMR 2023

Bibliographical note

Publisher Copyright:
©2023 Association for Computational Linguistics


This research was funded in part by grants from Intel Labs, the Planning and Budgeting Committee (PBC) of the Israeli Council for Higher Education under the National Data Science Competitive Program, and the Israel Science Foundation grant 2827/21.

FundersFunder number
Intel Labs
Israel Science Foundation2827/21
Council for Higher Education


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