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

Understand

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.