Dialogue State Tracking (DST) is a very complex task that requires precise understanding and information tracking of multi-domain conversations between users and dialogue systems. Many task-oriented dialogue systems use dialogue state tracking technology to infer users’ goals from the history of the conversation. Existing approaches for DST are usually conditioned on previous dialogue states. However, the dependency on previous dialogues makes it very challenging to prevent error propagation to subsequent turns of a dialogue. In this paper, we propose Neural Retrieval Augmentation to alleviate this problem by creating a Neural Index based on dialogue context. Our NRA-DST framework efficiently retrieves dialogue context from the index built using a combination of unstructured dialogue state and structured user/system utterances. We explore a simple pipeline resulting in a retrieval-guided generation approach for training a DST model. Experiments on different retrieval methods for augmentation show that neural retrieval augmentation is the best performing retrieval method for DST. Our evaluations on the large-scale MultiWOZ dataset show that our model outperforms the baseline approaches.

The main goal of this paper is to develop out-of-domain (OOD) detection for dialog systems. We propose to use only in-domain (IND) sentences to build a generative adversarial network (GAN) of which the discriminator generates low scores for OOD sentences. To improve basic GANs, we apply feature matching loss in the discriminator, use domain-category analysis as an additional task in the discriminator, and remove the biases in the generator. Thereby, we reduce the huge effort of collecting OOD sentences for training OOD detection. For evaluation, we experimented OOD detection on a multi-domain dialog system. The experimental results showed the proposed method was most accurate compared to the existing methods.