2016/06/28 by Ondřej Plátek, Plátek, Ondřej, Petr Bělohlávek +5
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Speech Recognition and Synthesis #Speech and dialogue systems #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1606.08733
openalex publication_date 2016/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.