Few-Shot Learning with Siamese Networks and Label Tuning

• URL: http://arxiv.org/abs/2203.14655v1
• Date: Mon, 28 Mar 2022 11:16:46 GMT
• Title: Few-Shot Learning with Siamese Networks and Label Tuning
• Authors: Thomas M\"uller and Guillermo P\'erez-Torr\'o and Marc Franco-Salvador
• Abstract summary: We show that with proper pre-training, Siamese Networks that embed texts and labels offer a competitive alternative. We introduce label tuning, a simple and computationally efficient approach that allows to adapt the models in a few-shot setup by only changing the label embeddings.
• Score: 5.006086647446482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification. In recent years, an approach based on neural textual entailment models has been found to give strong results on a diverse range of tasks. In this work, we show that with proper pre-training, Siamese Networks that embed texts and labels offer a competitive alternative. These models allow for a large reduction in inference cost: constant in the number of labels rather than linear. Furthermore, we introduce label tuning, a simple and computationally efficient approach that allows to adapt the models in a few-shot setup by only changing the label embeddings. While giving lower performance than model fine-tuning, this approach has the architectural advantage that a single encoder can be shared by many different tasks.

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