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

arXiv: https://arxiv.org/abs/2203.10659 · HTML: https://mlsanity.com/paper/2203.10659/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 64 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [Explainable Deep Learning: A Field Guide for the Uninitiated](https://mlsanity.com/paper/2004.14545/) (2020)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://mlsanity.com/paper/1911.04118/) (2019)
- [PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models](https://mlsanity.com/paper/2006.09075/) (2020)
- [MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction](https://mlsanity.com/paper/1904.08314/) (2019)
- [KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis](https://mlsanity.com/paper/2005.00791/) (2020)
- [Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation](https://mlsanity.com/paper/2005.11355/) (2020)
- [Adversarial Domain Adaptation for Stance Detection](https://mlsanity.com/paper/1902.02401/) (2019)
- [Stance detection in online discussions](https://mlsanity.com/paper/1701.00504/) (2017)

## Built on by (most cited first, 4 of 4)

- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [DAHRS: Divergence-Aware Hallucination-Remediated SRL Projection](https://mlsanity.com/paper/2407.09283/) (2024)
- [Revisiting Semantic Role Labeling: Efficient Structured Inference with Dependency-Informed Analysis](https://mlsanity.com/paper/2605.02505/) (2026)
- [Social Convos: Capturing Agendas and Emotions on Social Media](https://mlsanity.com/paper/2402.15571/) (2024)

## Abstract (arXiv)

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.