# Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation

## TL;DR

This paper introduces a novel out-of-domain detection method for dialog systems that uses counterfeit OOD turns without requiring OOD data, significantly improving robustness and performance in handling anomalous user inputs.

## Contribution

The paper proposes a new OOD detection approach using counterfeit data augmentation that does not depend on OOD data and releases new datasets for research.

## Key findings

- Outperforms existing models in OOD detection accuracy
- Effective in handling anomalous user inputs in dialogs
- Provides publicly available datasets for further research

## Abstract

Neural dialog models often lack robustness to anomalous user input and produce inappropriate responses which leads to frustrating user experience. Although there are a set of prior approaches to out-of-domain (OOD) utterance detection, they share a few restrictions: they rely on OOD data or multiple sub-domains, and their OOD detection is context-independent which leads to suboptimal performance in a dialog. The goal of this paper is to propose a novel OOD detection method that does not require OOD data by utilizing counterfeit OOD turns in the context of a dialog. For the sake of fostering further research, we also release new dialog datasets which are 3 publicly available dialog corpora augmented with OOD turns in a controllable way. Our method outperforms state-of-the-art dialog models equipped with a conventional OOD detection mechanism by a large margin in the presence of OOD utterances.