Using phone features to improve dialogue state tracking generalisation to unseen states

Abstract

The generalisation of dialogue state tracking to unseen dialogue states can be very challenging. In a slot-based dialogue system, dialogue states lie in discrete space where distances between states cannot be computed. Therefore, the model parameters to track states unseen in the training data can only be estimated from more general statistics, under the assumption that every dialogue state will have the same underlying state tracking behaviour. However, this assumption is not valid. For example, two values, whose associated concepts have different ASR accuracy, may have different state tracking performance. Therefore, if the ASR performance of the concepts related to each value can be estimated, such estimates can be used as general features. The features will help to relate unseen dialogue states to states seen in the training data with similar ASR performance. Furthermore, if two phonetically similar concepts have similar ASR performance, the features extracted from the phonetic structure of the concepts can be used to improve generalisation. In this paper, ASR and phonetic structure-related features are used to improve the dialogue state tracking generalisation to unseen states of an environmental control system developed for dysarthric speakers.