Comunicació presentada a SIGDIAL 2017 Conference, the 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue, celebrada del 15 al 17 d'agost a Saarbrucken, Alemanya.

For estimating the Interaction Quality (IQ) in Spoken Dialogue Systems (SDS), the dialogue history is of significant importance. Previous works included this information manually in the form of precomputed temporal features into the classification process. Here, we employ a deep learning architecture based on Long Short-Term Memories (LSTM) to extract this information automatically from the data, thus estimating IQ solely by using current exchange features. We show that it is thereby possible to achieve competitive results as in a scenario where manually optimized temporal features have been included.

This work is part of a project that has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 645012.