Deep Neural Networks (DNN) models have achieved acceptable performance in sentiment prediction of written text. However, the output of these machine learning (ML) models cannot be natively interpreted. In this paper, we study how the sentiment polarity predictions by DNNs can be explained and compare them to humans’ explanations. We crowdsource a corpus of Personal Narratives and ask human judges to annotate them with polarity and select the corresponding token chunks - the Emotion Carriers (EC) - that convey narrators’ emotions in the text. The interpretations of ML neural models are carried out through Integrated Gradients method and we compare them with human annotators’ interpretations. The results of our comparative analysis indicate that while the ML model mostly focuses on the explicit appearance of emotions-laden words (e.g. happy, frustrated), the human annotator predominantly focuses the attention on the manifestation of emotions through ECs that denote events, persons, and objects which activate narrator’s emotional state.

The acquisition of a dialogue corpus is a key step in the process of training a dialogue model. In this context, corpora acquisitions have been designed either for open-domain information retrieval or slot-filling (e.g. restaurant booking) tasks. However, there has been scarce research in the problem of collecting personal conversations with users over a long period of time. In this paper we focus on the types of dialogues that are required for mental health applications. One of these types is the follow-up dialogue that a psychotherapist would initiate in reviewing the progress of a Cognitive Behavioral Therapy (CBT) intervention. The elicitation of the dialogues is achieved through textual stimuli presented to dialogue writers. We propose an automatic algorithm that generates textual stimuli from personal narratives collected during psychotherapy interventions. The automatically generated stimuli are presented as a seed to dialogue writers following principled guidelines. We analyze the linguistic quality of the collected corpus and compare the performances of psychotherapists and non-expert dialogue writers. Moreover, we report the human evaluation of a corpus-based response-selection model.