Neural Lexicons for Slot Tagging in Spoken Language Understanding


We explore the use of lexicons in neural models for slot tagging in spoken language understanding. We develop models that encode lexicon information as features for use in a Long-short term memory neural network. Experiments are performed on data from 4 domains from an intelligent assistant under conditions that often occur in an industry setting, where there may be: 1) large amounts of training data, 2) limited amounts of training data for new domains, and 3) cross domain training. Results show that the use of neural lexicon information leads to a significant improvement in slot tagging, with improvements in the F-score of up to 12%.

2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL ‘19)