Dataset Augmentation Using Back-Translation to Improve Early Stage Dialog Systems

Abstract

As dialog systems are increasingly used, a major challenge for building new ones is the lack of annotated training data. The necessary data collection and annotation efforts are laborious and time-consuming. A potential solution is to augment initial seed data by automatically paraphrasing existing samples. In this paper, we propose a novel dataefficient approach towards this goal. Our method can kick-start a dialog system with minimum human effort while delivering a performance strong enough to allow real-world usage. We ran experiments using Neural Machine Translation on two open corpora. On both of them, the proposed approach improved the generalization capabilities of the model. Our results suggest that paraphrase generation techniques could be used as-is to provide a boost in performance to dialog systems in an early phase.