Spoken language understanding (SLU) systems are widely used in handling of customer-care calls. A traditional SLU system consists of an acoustic model (AM) and a language model (LM) that are used to decode the utterance and a natural language understanding (NLU) model that predicts the intent. While AM can be shared across different domains, LM and NLU models need to be trained specifically for every new task. However, preparing enough data to train these models is prohibitively expensive. In this paper, we introduce an efficient method to expand the limited in-domain data. The process starts with training a preliminary NLU model based on logistic regression on the in-domain data. Since the features are based on n = 1, 2-grams, we can detect the most informative n-grams for each intent class. Using these n-grams, we find the samples in the out-of-domain corpus that 1) contain the desired n-gram and/or 2) have similar intent label. The ones which meet the first constraint are used to train a new LM model and the ones that meet both constraints are used to train a new NLU model. Our results on two divergent experimental setups show that the proposed approach reduces by 30% the absolute classification error rate (CER) comparing to the preliminary models and it significantly outperforms the traditional data expansion algorithms such as the ones based on semi-supervised learning, TF-IDF and embedding vectors.
REF introduce a technique to expand the limited in-domain data for a new spoken language understanding task.
Automatic Data Expansion for Customer-care Spoken Language Understanding
ArXiv
ArXiv
Computer Science