Thomas Dopierre, Christophe Gravier, Wilfried Logerais. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Modern classification models tend to struggle when the amount of annotated data is scarce.To overcome this issue, several neural fewshot classification models have emerged, yielding significant progress over time, both in Computer Vision and Natural Language Processing.In the latter, such models used to rely on fixed word embeddings before the advent of transformers.Additionally, some models used in Computer Vision are yet to be tested in NLP applications.In this paper, we compare all these models, first adapting those made in the field of image processing to NLP, and second providing them access to transformers.We then test these models equipped with the same transformer-based encoder on the intent detection task, known for having a large number of classes.Our results reveal that while methods perform almost equally on the ARSC dataset, this is not the case for the Intent Detection task, where the most recent and supposedly best competitors perform worse than older and simpler ones (while all are given access to transformers).We also show that a simple baseline is surprisingly strong.All the new developed models, as well as the evaluation framework, are made publicly available 1 .

In this paper, we introduce a state-of-the-art pseudo-labeling technique for few-shot intent detection.We devise a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances.We show that our two-step method yields significant improvement over existing solutions.This performance is achieved on multiple intent detection datasets, even in more challenging situations where the number of classes is large or when the dataset is highly imbalanced.Moreover, we confirm these results on the more general text classification task.We also demonstrate that our approach nicely complements existing solutions, thereby providing an even stronger state-of-the-art ensemble method.We make our code publicly available 1 for future research.