Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information
This addresses the challenge of generating effective training data for intent detection in few-shot scenarios, though it is incremental as it builds on existing augmentation and filtering techniques.
The paper tackles the problem of in-context data augmentation for intent detection by introducing a method that uses large pre-trained language models and pointwise V-information to filter unhelpful synthetic data, resulting in state-of-the-art performance with improvements of 1.28% in 5-shot and 1.18% in 10-shot settings on average.
This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a small seed of training data and then synthesizes new datapoints - utterances that correspond to given intents. It then employs intent-aware filtering, based on PVI, to remove datapoints that are not helpful to the downstream intent classifier. Our method is thus able to leverage the expressive power of large language models to produce diverse training data. Empirical results demonstrate that our method can produce synthetic training data that achieve state-of-the-art performance on three challenging intent detection datasets under few-shot settings (1.28% absolute improvement in 5-shot and 1.18% absolute in 10-shot, on average) and perform on par with the state-of-the-art in full-shot settings (within 0.01% absolute, on average).