Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding

Abstract

One of the main challenges in Spoken Language Understanding (SLU) is dealing with `open-vocabulary' slots. Recently, SLU models based on neural network were proposed, but it is still difficult to recognize the slots of unknown words or `open-vocabulary' slots because of the high cost of creating a manually tagged SLU dataset. This paper proposes data noising, which reflects the characteristics of the `open-vocabulary' slots, for data augmentation. We applied it to an attention based bi-directional recurrent neural network (Liu and Lane, 2016) and experimented with three datasets: Airline Travel Information System (ATIS), Snips, and MIT-Restaurant. We achieved performance improvements of up to 0.57\% and 3.25 in intent prediction (accuracy) and slot filling (f1-score), respectively. Our method is advantageous because it does not require additional memory and it can be applied simultaneously with the training process of the model.

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Reproductions