Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems

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.Comment: Published in CIKM '19, 10 page

Effect of patient-focused clinical pathway on anxiety, depression and satisfaction of patients with coronary artery disease: A quasi-experimental study

Background: Coronary artery diseases (CAD) are associated with psychological problems such as anxiety and depression in patients. Thus, management of these problems can consider as an important intervention by health care workers, especially nurses. Objectives: The purpose of this study was to investigate the effectiveness of patient-focused clinical pathway on anxiety, depression and satisfaction of patients with CAD. Patients and Methods: In this quasi-experimental study, 138 patients suffering from CAD in a coronary care unit of a referral teaching hospital affiliated to Semnan University of Medical Sciences in Semnan, Iran, were recruited using a convenience sampling method. The participants were assigned to two groups: Clinical pathway (CP) and routine (RUT) care. The level of anxiety and depression of patients were measured in admission and discharge in both groups. Also, the level of patients� satisfaction was measured at the time of discharge. Data were analyzed using descriptive and inferential statistics. Results: Prevalence rates of anxiety and depression in total of patients were 7.2 and 8.7, respectively. In terms of anxiety, the mean of difference between pretest and posttest scores in the CP group (0.52 ± 1.39) was higher compared to the RUT group (-0.17 ± 1.69) and there was a significant difference between the two group (P = 0.009). In terms of depression, the mean of this difference in the CP group (0.75 ± 2.05) was higher compared to the RUT group (0.00 ± 1.08), as there was a significant difference between the two group (P = 0.024). Also, the mean of patients� satisfaction scores in the CP group (3.69 ± 0.39) was higher compared to the RUT group (3.45 ± 0.47) and there was a significant difference between the two groups (P = 0.002). Conclusions: According to the positive effects of CP on patients with CADs, it can be considered as a useful, safe and simple instrument for the improvement of patients� outcomes. Thus, the findings of this study can provide a new insight in patient care for clinical nurses. © 2015, Iranian Red Crescent Medical Journal