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Zero-shot stance detection: Paradigms and challenges.

Emily Allaway¹, Kathleen McKeown¹

¹Department of Computer Science, Columbia University, New York, NY, United States.

Frontiers in Artificial Intelligence

|January 30, 2023

Summary

Expanding zero-shot stance detection to multilingual and multi-genre settings is crucial. This research advocates for improved model explainability and robust evaluations for diverse language and topic challenges.

Keywords:

domain adaptation multilingual stance detection transfer learning zero-shot

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Area of Science:

Natural Language Processing
Computational Linguistics
Artificial Intelligence

Background:

Stance detection faces challenges with a vast and evolving range of topics, languages, and genres.
Collecting annotated data for all potential topics is impractical and costly.
Current research on zero-shot stance detection primarily focuses on English, neglecting multilingual and multi-genre scenarios.

Purpose of the Study:

To highlight the need for expanding zero-shot stance detection beyond English to encompass multilingual and multi-genre contexts.
To review existing paradigms and recent advancements in English zero-shot stance detection.
To propose best practices for future research in multilingual and multi-genre zero-shot stance detection.

Main Methods:

Discussion of two established paradigms for English zero-shot stance detection evaluation.
Review of recent literature on multilingual and multi-genre stance detection (primarily non-zero-shot).
Argument for adapting domain adaptation techniques and enhancing model explainability.

Main Results:

Identified a gap in research concerning multilingual and multi-genre zero-shot stance detection.
Highlighted the limitations of current approaches in handling diverse linguistic and topical variations.
Emphasized the importance of robust evaluation metrics beyond generalization ability.

Conclusions:

Zero-shot stance detection research must broaden to include diverse languages and genres to reflect real-world complexities.
Future work should prioritize developing models that are explainable and evaluated rigorously across various linguistic contexts.
Systematic approaches and best practices are needed to advance the field of multilingual and multi-genre zero-shot stance detection.