  Predicting user satisfaction in conversational systems has become critical,
as spoken conversational assistants operate in increasingly complex domains.
Online satisfaction prediction (i.e., predicting satisfaction of the user with
the system after each turn) could be used as a new proxy for implicit user
feedback, and offers promising opportunities to create more responsive and
effective conversational agents, which adapt to the user's engagement with the
agent. To accomplish this goal, we propose a conversational satisfaction
prediction model specifically designed for open-domain spoken conversational
agents, called ConvSAT. To operate robustly across domains, ConvSAT aggregates
multiple representations of the conversation, namely the conversation history,
utterance and response content, and system- and user-oriented behavioral
signals. We first calibrate ConvSAT performance against state of the art
methods on a standard dataset (Dialogue Breakdown Detection Challenge) in an
online regime, and then evaluate ConvSAT on a large dataset of conversations
with real users, collected as part of the Alexa Prize competition. Our
experimental results show that ConvSAT significantly improves satisfaction
prediction for both offline and online setting on both datasets, compared to
the previously reported state-of-the-art approaches. The insights from our
study can enable more intelligent conversational systems, which could adapt in
real-time to the inferred user satisfaction and engagement.
