MixOut: A Simple Yet Effective Data Augmentation Scheme for Slot-Filling

Abstract

We present a data augmentation strategy for slot-filling in task-oriented dialogue systems. It is simple yet effective and does not rely on external corpora. Lexicons for all slot types are generated from available annotated data. Synthetic, yet realistic utterances are then created by replacing slot values with other values of the same type. The method can also be easily extended to synthesize mixed language utterances for cross-lingual training. Monolingual experiments on 14 datasets across 10 different domains, 4 languages and cross-lingual experiments on 3 language pairs demonstrate the effectiveness of this method.