Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems

Ankur Padia, Francis Ferraro, Tim Finin


Abstract
Moral values as commonsense norms shape our everyday individual and community behavior. The possibility to extract moral attitude rapidly from natural language is an appealing perspective that would enable a deeper understanding of social interaction dynamics and the individual cognitive and behavioral dimension. In this work we focus on detecting moral content from natural language and we test our methods on a corpus of tweets previously labeled as containing moral values or violations, according to Moral Foundation Theory. We develop and compare two different approaches: (i) a frame-based symbolic value detector based on knowledge graphs and (ii) a zero-shot machine learning model fine-tuned on a task of Natural Language Inference (NLI) and a task of emotion detection. The final outcome from our work consists in two approaches meant to perform without the need for prior training process on a moral value detection task.
Anthology ID:
2022.deelio-1.5
Volume:
Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
Month:
May
Year:
2022
Address:
Dublin, Ireland and Online
Editors:
Eneko Agirre, Marianna Apidianaki, Ivan Vulić
Venue:
DeeLIO
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
42–52
Language:
URL:
https://aclanthology.org/2022.deelio-1.5
DOI:
10.18653/v1/2022.deelio-1.5
Bibkey:
Cite (ACL):
Ankur Padia, Francis Ferraro, and Tim Finin. 2022. Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems. In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 42–52, Dublin, Ireland and Online. Association for Computational Linguistics.
Cite (Informal):
Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems (Padia et al., DeeLIO 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.deelio-1.5.pdf
Video:
 https://aclanthology.org/2022.deelio-1.5.mp4
Data
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