# HyST: A Hybrid Approach for Flexible and Accurate Dialogue State Tracking

## TL;DR

HyST is a hybrid dialogue state tracking method that combines the strengths of probability distribution and candidate-generation approaches, improving accuracy and scalability in multi-domain dialogue systems.

## Contribution

This paper introduces HyST, a novel hybrid approach that learns to select the best method for each slot type, enhancing performance on large, multi-domain datasets.

## Key findings

- HyST outperforms previous state-of-the-art methods by 24%
- HyST demonstrates scalability to multi-domain applications
- The hybrid approach improves accuracy over individual methods

## Abstract

Recent works on end-to-end trainable neural network based approaches have demonstrated state-of-the-art results on dialogue state tracking. The best performing approaches estimate a probability distribution over all possible slot values. However, these approaches do not scale for large value sets commonly present in real-life applications and are not ideal for tracking slot values that were not observed in the training set. To tackle these issues, candidate-generation-based approaches have been proposed. These approaches estimate a set of values that are possible at each turn based on the conversation history and/or language understanding outputs, and hence enable state tracking over unseen values and large value sets however, they fall short in terms of performance in comparison to the first group. In this work, we analyze the performance of these two alternative dialogue state tracking methods, and present a hybrid approach (HyST) which learns the appropriate method for each slot type. To demonstrate the effectiveness of HyST on a rich-set of slot types, we experiment with the recently released MultiWOZ-2.0 multi-domain, task-oriented dialogue-dataset. Our experiments show that HyST scales to multi-domain applications. Our best performing model results in a relative improvement of 24% and 10% over the previous SOTA and our best baseline respectively.