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

• URL: http://arxiv.org/abs/2105.12995v1
• Date: Thu, 27 May 2021 08:31:27 GMT
• Title: ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning
• Authors: Thomas Dopierre, Christophe Gravier, Wilfried Logerais
• Abstract summary: We propose ProtAugment, a meta-learning algorithm for intent detection. ProtAugment is a novel extension of Prototypical Networks.
• Score: 4.689945062721168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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