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Conference Paper
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding
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- Authors
- Hwa-Yeon Kim, Yoon-Hyung Roh, Young-Kil Kim
- Issue Date
- 2019-06
- Citation
- Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) 2019: Student Research Workshop (SRW), pp.97-102
- Language
- English
- Type
- Conference Paper
- 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.
- KSP Keywords
- Bi-directional, Data Augmentation, F1-score, Information systems(IS), Intent prediction, Recurrent Neural Network(RNN), Spoken language understanding, Travel information, Unknown words, slot filling, training process