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.
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.
@cite_17
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One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users' language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.