From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains

Brodie Mather, Bonnie Dorr, Adam Dalton, William de Beaumont, Owen Rambow, Sonja Schmer-Galunder


Abstract
We present a generalized paradigm for adaptation of propositional analysis (predicate-argument pairs) to new tasks and domains. We leverage an analogy between stances (belief-driven sentiment) and concerns (topical issues with moral dimensions/endorsements) to produce an explanatory representation. A key contribution is the combination of semi-automatic resource building for extraction of domain-dependent concern types (with 2-4 hours of human labor per domain) and an entirely automatic procedure for extraction of domain-independent moral dimensions and endorsement values. Prudent (automatic) selection of terms from propositional structures for lexical expansion (via semantic similarity) produces new moral dimension lexicons at three levels of granularity beyond a strong baseline lexicon. We develop a ground truth (GT) based on expert annotators and compare our concern detection output to GT, to yield 231% improvement in recall over baseline, with only a 10% loss in precision. F1 yields 66% improvement over baseline and 97.8% of human performance. Our lexically based approach yields large savings over approaches that employ costly human labor and model building. We provide to the community a newly expanded moral dimension/value lexicon, annotation guidelines, and GT.
Anthology ID:
2022.findings-acl.264
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3354–3367
Language:
URL:
https://aclanthology.org/2022.findings-acl.264
DOI:
10.18653/v1/2022.findings-acl.264
Bibkey:
Cite (ACL):
Brodie Mather, Bonnie Dorr, Adam Dalton, William de Beaumont, Owen Rambow, and Sonja Schmer-Galunder. 2022. From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3354–3367, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains (Mather et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.264.pdf
Code
 ihmc/findings-of-acl-2022-concern-detection