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Recent research considers few-shot intent detection as a meta-learningproblem: the model is learning to learn from a consecutive set of small tasksnamed episodes. In this work, we propose ProtAugment, a meta-learning algorithmfor short texts classification (the intent detection task). ProtAugment is anovel extension of Prototypical Networks, that limits overfitting on the biasintroduced by the few-shots classification objective at each episode. It relieson diverse paraphrasing: a conditional language model is first fine-tuned forparaphrasing, and diversity is later introduced at the decoding stage at eachmeta-learning episode. The diverse paraphrasing is unsupervised as it isapplied to unlabelled data, and then fueled to the Prototypical Networktraining objective as a consistency loss. ProtAugment is the state-of-the-artmethod for intent detection meta-learning, at no extra labeling efforts andwithout the need to fine-tune a conditional language model on a givenapplication domain.

ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning