Efficient parsing of spoken inputs for human-robot interaction

Abstract

The use of deep parsers in spoken dialogue systems is usually subject to strong performance requirements. This is particularly the case in human-robot interaction, where the computing resources are limited and must be shared by many components in parallel. A real-time dialogue system must be capable of responding quickly to any given utterance, even in the presence of noisy, ambiguous or distorted input. The parser must therefore ensure that the number of analyses remains bounded at every processing step. The paper presents a practical approach to addressing this issue in the context of deep parsers designed for spoken dialogue. The approach is based on a word lattice parser combined with a statistical model for parse selection. Each word lattice is parsed incrementally, word by word, and a discriminative model is applied at each incremental step to prune the set of resulting partial analyses. The model incorporates a wide range of linguistic and contextual features and can be trained with a simple perceptron. The approach is fully implemented as part of a spoken dialogue system for human-robot interaction. Evaluation results on a Wizard-of-Oz test suite demonstrate significant improvements in parsing time.