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

By Hwa-Yeon Kim, Yoon-Hyung Roh, Young-Kil Kim. 2019

16 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, background): “Hou et al. (2018), Kim et al. (2019), Zhao et al. (2019), and Yoo et al. (2019) investigate DA methods for dialogue and spoken language understanding (SLU), including generative latent variable models.”
Paraphrase Augmented Task-Oriented Dialog Generation (2020, background): “Such an idea led to recent augmentation work on the language understanding task (Hou et al., 2018; Kim et al., 2019; Yoo et al., 2019; Zhao et al., 2019).”