Pragst, LouisaUltes, StefanMinker, Wolfgang2017-09-262016Pragst L, Ultes S, Minker W. Recurrent neural network interaction quality estimation. In: Jokinen K, Wilcock G, editors. Dialogues with social robots: enablements, analyses, and evaluation. Singapore: Springer; 2016. p. 381-93. (LNEE; no. 427). DOI: 10.1007/978-981-10-2585-3_31http://hdl.handle.net/10230/32817Getting a good estimation of the Interaction Quality (IQ) of a spoken dialogue helps to increase the user satisfaction as the dialogue strategy may be adapted accordingly. Therefore, some research has already been conducted in order to automatically estimate the Interaction Quality. This paper adds to this by describing how Recurrent Neural Networks may be used to estimate the Interaction Quality for each dialogue turn and by evaluating their performance on this task. Here, we will show that RNNs may outperform non-recurrent neural networks.application/pdfeng© Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-981-10-2585-3_31info:eu-repo/semantics/bookParthttp://dx.doi.org/10.1007/978-981-10-2585-3_31RNNSequential dataQuality of dialogueRecurrent neural networkNeural networkInteraction qualityUser satisfactionSpoken dialogue systeminfo:eu-repo/semantics/openAccess