Recurrent Neural Networks for Dialogue State Tracking
This addresses the problem of dialogue state tracking for conversational AI systems, but it is incremental as it builds on existing RNN approaches with minor improvements.
The paper tackles dialogue state tracking by proposing recurrent neural network models that work in incremental settings with minimal preprocessing, achieving performance close to state-of-the-art on the DSTC2 dataset.
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.