  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.
