Towards Improving Selective Prediction Ability of NLP Systems
Neeraj Varshney, Swaroop Ranjan Mishra, Chitta R. Baral · 2022
It's better to say "I can't answer" than to answer incorrectly.This selective prediction ability is crucial for NLP systems to be reliably deployed in real-world applications.Prior work has shown that existing selective prediction techniques fail to perform well, especially in the out-of-domain setting.In this work, we propose a method that improves probability estimates of models by calibrating them using prediction confidence and difficulty score of instances.Using these two signals, we first annotate held-out instances and then train a calibrator to predict the likelihood of correctness of the model's prediction.We instantiate our method with Natural Language Inference (NLI) and Duplicate Detection (DD) tasks and evaluate it in both In-Domain (IID) and Outof-Domain (OOD) settings.In (IID, OOD) settings, we show that the representations learned by our calibrator result in an improvement of (15.81%, 5.64%) and (6.19%, 13.9%) over MaxProb -a selective prediction baseline-on NLI and DD tasks respectively.