PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation

TL;DR

This paper introduces PnPOOD, a novel data augmentation method using plug-and-play language models to improve out-of-distribution detection in NLP, addressing data leakage and calibration issues present in prior approaches.

Contribution

The paper proposes PnPOOD, a new data augmentation technique leveraging plug-and-play language models for more accurate and better-calibrated OOD detection in text classification.

Findings

Outperforms prior OOD detection models on benchmark datasets.

Exhibits lower calibration error in experiments.

Identifies and addresses data leakage issues in existing datasets.

Abstract

While Out-of-distribution (OOD) detection has been well explored in computer vision, there have been relatively few prior attempts in OOD detection for NLP classification. In this paper we argue that these prior attempts do not fully address the OOD problem and may suffer from data leakage and poor calibration of the resulting models. We present PnPOOD, a data augmentation technique to perform OOD detection via out-of-domain sample generation using the recently proposed Plug and Play Language Model (Dathathri et al., 2020). Our method generates high quality discriminative samples close to the class boundaries, resulting in accurate OOD detection at test time. We demonstrate that our model outperforms prior models on OOD sample detection, and exhibits lower calibration error on the 20 newsgroup text and Stanford Sentiment Treebank dataset (Lang, 1995; Socheret al., 2013). We further highlight an important data leakage issue with datasets used in prior attempts at OOD detection, and share results on a new dataset for OOD detection that does not suffer from the same problem.