A Portability Study on Natural Language Call Steering

In this paper we examine the portability of the vector-based call router to a new task involving calls to the operator in the UK. One component of the router was shown to require expert knowledge and hand-tuning: the stop word list. Stop word filtering involves replacing certain words with place markers and is necessary to reduce the number of features and parameters used by the classifier. Two specific approaches that eliminate the need for stop word filtering were investigated that led to comparable classification performance: (1) using trigram, bigram, and unigram features and using SVD to reduce the number of parameters, and (2) using only unigram features and applying discriminative training to boost the performance. After discriminative training, the classification error rate was reduced by 18-30% over the baseline unigram results. Increased robustness is demonstrated by a 24-48% reduction in error rate at 20% false rejection rate.