text: This paper discusses models for dialogue state tracking using recurrent neural networks (RNN). We present experiments on the standard dialogue state tracking (DST) dataset, DSTC2. On the one hand, RNN models became the state of the art models in DST, on the other hand, most state-of-the-art models are only turn-based and require dataset-specific preprocessing (e.g. DSTC2-specific) in order to achieve such results. We implemented two architectures which can be used in incremental settings and require almost no preprocessing. We compare their performance to the benchmarks on DSTC2 and discuss their properties. With only trivial preprocessing, the performance of our models is close to the state-of- the-art results.
  @cite_17: While belief tracking is known to be important in allowing statistical dialog systems to manage dialogs in a highly robust manner, until recently little attention has been given to analysing the behaviour of belief tracking techniques. The Dialogue State Tracking Challenge has allowed for such an analysis, comparing multiple belief tracking approaches on a shared task. Recent success in using deep learning for speech research motivates the Deep Neural Network approach presented here. The model parameters can be learnt by directly maximising the likelihood of the training data. The paper explores some aspects of the training, and the resulting tracker is found to perform competitively, particularly on a corpus of dialogs from a system not found in the training.
summary: Our system is related to the RNN tracker of , which reported near state-of-the art results on the DSTC2 dataset and introduced the first incremental system which was able to update the dialogue state word-by-word with such accuracy. In contrast to work of , we use no abstraction of slot values. Instead, we add the additional features as described in . The first system which used a neural network for dialogue state tracking @cite_17 used a feed-forward network and more than 10 manually engineered features across different levels of abstraction of the user input, including the outputs of the spoken language understanding component (SLU). In our work, we focus on simplifying the architecture, hence we used only features which were explicitly given by the dialogue history word representation and the database.