Automatic Data Expansion for Customer-care Spoken Language Understanding

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 areused to decode the utterance and a natural language understanding (NLU) model that predicts theintent. While AM can be shared across different domains, LM and NLU models need to be trainedspecifically for every new task. However, preparing enough data to train these models is prohibitivelyexpensive. In this paper, we introduce an efficient method to expand the limited in-domain data. Theprocess starts with training a preliminary NLU model based on logistic regression on the in-domaindata. Since the features are based onn= 1,2-grams, we can detect the most informative n-gramsfor each intent class. Using these n-grams, we find the samples in the out-of-domain corpus that1) contain the desired n-gram and/or 2) have similar intent label. The ones which meet the firstconstraint are used to train a new LM model and the ones that meet both constraints are used to train anew NLU model. Our results on two divergent experimental setups show that the proposed approachreduces by 30% the absolute classification error rate (CER) comparing to the preliminary modelsand it significantly outperforms the traditional data expansion algorithms such as the ones based onsemi-supervised learning, TF-IDF and embedding vectors.Comment: 10 pages, 4 figures, 5 tabel

Unsupervised Spoken Utterance Classification

An intelligent virtual assistant (IVA) enables effortless conversations in call routing through spoken utterance classification (SUC) which is a special form of spoken language understanding (SLU). Building a SUC system requires a large amount of supervised in-domain data that is not always available. In this paper, we introduce an unsupervised spoken utterance classification approach (USUC) that does not require any in-domain data except for the intent labels and a few para-phrases per intent. USUC is consisting of a KNN classifier (K=1) and a complex embedding model trained on a large amount of unsupervised customer service corpus. Among all embedding models, we demonstrate that Elmo works best for USUC. However, an Elmo model is too slow to be used at run-time for call routing. To resolve this issue, first, we compute the uni- and bi-gram embedding vectors offline and we build a lookup table of n-grams and their corresponding embedding vector. Then we use this table to compute sentence embedding vectors at run-time, along with back-off techniques for unseen n-grams. Experiments show that USUC outperforms the traditional utterance classification methods by reducing the classification error rate from 32.9% to 27.0% without requiring supervised data. Moreover, our lookup and back-off technique increases the processing speed from 16 utterances per second to 118 utterances per second.Comment: 4 page