Detecting Statements in Text: A Domain-Agnostic Few-Shot Solution
<|reference_start|>Detecting Statements in Text: A Domain-Agnostic Few-Shot Solution: Many tasks related to Computational Social Science and Web Content Analysis involve classifying pieces of text based on the claims they contain. State-of-the-art approaches usually involve fine-tuning models on large annotated datasets, which are costly to produce. In light of this, we propose and release a qualitative and versatile few-shot learning methodology as a common paradigm for any claim-based textual classification task. This methodology involves defining the classes as arbitrarily sophisticated taxonomies of claims, and using Natural Language Inference models to obtain the textual entailment between these and a corpus of interest. The performance of these models is then boosted by annotating a minimal sample of data points, dynamically sampled using the well-established statistical heuristic of Probabilistic Bisection. We illustrate this methodology in the context of three tasks: climate change contrarianism detection, topic/stance classification and depression-relates symptoms detection. This approach rivals traditional pre-train/fine-tune approaches while drastically reducing the need for data annotation.<|reference_end|>
@article{chausson2024detecting,
    title={Detecting Statements in Text: A Domain-Agnostic Few-Shot Solution},
    author={Sandrine Chausson and Bj"orn Ross},
    journal={arXiv preprint arXiv:2405.05705},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05705},
    primaryClass={cs.CL}
}