Recent research considers few-shot intent detection as a meta-learning problem: the model is learning to learn from a consecutive set of small tasks named episodes. In this work, we propose ProtAugment, a meta-learning algorithm for short texts classification (the intent detection task). ProtAugment is a novel extension of Prototypical Networks, that limits overfitting on the bias introduced by the few-shots classification objective at each episode. It relies on diverse paraphrasing: a conditional language model is first fine-tuned for paraphrasing, and diversity is later introduced at the decoding stage at each meta-learning episode. The diverse paraphrasing is unsupervised as it is applied to unlabelled data, and then fueled to the Prototypical Network training objective as a consistency loss. ProtAugment is the state-of-the-art method for intent detection meta-learning, at no extra labeling efforts and without the need to fine-tune a conditional language model on a given application domain.
@article{arxiv.2105.12995,
title = {ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning},
author = {Thomas Dopierre and Christophe Gravier and Wilfried Logerais},
journal= {arXiv preprint arXiv:2105.12995},
year = {2021}
}
Comments
Accepted at the 59th Annual Meeting of the Association for Computational Linguistics (ACL2021)