GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation

• URL: http://arxiv.org/abs/2109.03079v1
• Date: Tue, 7 Sep 2021 13:35:03 GMT
• Title: GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation
• Authors: Derek Chen, Zhou Yu
• Abstract summary: Gold technique augments existing data to train better OOS detectors operating in low-data regimes. In experiments across three target benchmarks, the top GOLD model outperforms all existing methods on all key metrics.
• Score: 41.04593978694591
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Practical dialogue systems require robust methods of detecting out-of-scope (OOS) utterances to avoid conversational breakdowns and related failure modes. Directly training a model with labeled OOS examples yields reasonable performance, but obtaining such data is a resource-intensive process. To tackle this limited-data problem, previous methods focus on better modeling the distribution of in-scope (INS) examples. We introduce GOLD as an orthogonal technique that augments existing data to train better OOS detectors operating in low-data regimes. GOLD generates pseudo-labeled candidates using samples from an auxiliary dataset and keeps only the most beneficial candidates for training through a novel filtering mechanism. In experiments across three target benchmarks, the top GOLD model outperforms all existing methods on all key metrics, achieving relative gains of 52.4%, 48.9% and 50.3% against median baseline performance. We also analyze the unique properties of OOS data to identify key factors for optimally applying our proposed method.

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