Towards Few-Shot Identification of Morality Frames using In-Context Learning

Shamik Roy, Nishanth Sridhar Nakshatri, Dan Goldwasser

Abstract
Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge. As a result, few-shot identification of these concepts is desirable. Few-shot in-context learning using pre-trained Large Language Models (LLMs) has been recently applied successfully in many NLP tasks. In this paper, we study few-shot identification of a psycho-linguistic concept, Morality Frames (Roy et al., 2021), using LLMs. Morality frames are a representation framework that provides a holistic view of the moral sentiment expressed in text, identifying the relevant moral foundation (Haidt and Graham, 2007) and at a finer level of granularity, the moral sentiment expressed towards the entities mentioned in the text. Previous studies relied on human annotation to identify morality frames in text which is expensive. In this paper, we propose prompting based approaches using pretrained Large Language Models for identification of morality frames, relying only on few-shot exemplars. We compare our models’ performance with few-shot RoBERTa and found promising results.
Anthology ID:
2022.nlpcss-1.20
Volume:
Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)
Month:
November
Year:
2022
Address:
Abu Dhabi, UAE
Venue:
NLP+CSS
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Publisher:
Association for Computational Linguistics
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Pages:
183–196
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URL:
https://aclanthology.org/2022.nlpcss-1.20
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Cite (ACL):
Shamik Roy, Nishanth Sridhar Nakshatri, and Dan Goldwasser. 2022. Towards Few-Shot Identification of Morality Frames using In-Context Learning. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), pages 183–196, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Towards Few-Shot Identification of Morality Frames using In-Context Learning (Roy et al., NLP+CSS 2022)
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