Semantic parser enhancement for dialogue domain extension with little data

Abstract

Statistical semantic parser trained on sufficient in-domain data has shown robustness to speech recognition errors in end-to-end spoken dialogue systems. However, when the dialogue domain is extended, due to the introduction of new semantic slots, values and unknown speech pattern, the parsing performance may significantly degrade. Effective re-training of statistical semantic parser is therefore important. This paper describes a novel semantic parser enhancement approach for domain extension with very little new data. It employs automatic pseudo-data generation for parser re-training and domain independent rescoring to further improve parsing performance. The approach was evaluated on the DSTC3 (the third Dialog State Tracking Challenge) data corpus. Experiments showed that the proposed approach can yield consistent and significant improvements across all metrics of semantic parsing and dialog state tracking.