Customer-support services increasingly rely on automation, whether fully or with human intervention. Despite optimising resources, this may result in mechanical protocols and lack of human interaction, thus reducing customer loyalty. Our goal is to enhance interpretability and provide guidance in communication through novel tools for easier analysis of message trends and sentiment variations. Monitoring these contributes to more informed decision-making, enabling proactive mitigation of potential issues, such as protocol deviations or customer dissatisfaction. We propose a generic approach for dialogue flow discovery that leverages clustering techniques to identify dialogue states, represented by related utterances. State transitions are further analyzed to detect prevailing sentiments. Hence, we discover sentiment-aware dialogue flows that offer an interpretability layer to artificial agents, even those based on black-boxes, ultimately increasing trustworthiness. Experimental results demonstrate the effectiveness of our approach across different dialogue datasets, covering both human-human and human-machine exchanges, applicable in task-oriented contexts but also to social media, highlighting its potential impact across various customer-support settings.

Today, human assistants are often replacedby chatbots, designed to communicate via natural language, however, some disadvantages are notorious with this replacement. This PhD thesis project consists of researching, implementing, and testing a solution for guiding the action of a human in a contact center. It will start with the discovery and creation of datasets in Portuguese.Next, it will go through three main components: Extraction for processing dialogs and using the information todescribe interactions; Representation for discovering the most frequent dialog flowsrepresented by graphs; Guidance for helping the agent during a new dialog. These will be integrated in a single framework. In order to avoid service degradation resulting from the adoption of chatbots, this work aims to explore technologies in order to increase the efficiency of the human’s job without losing human contact.

Several dialogue corpora are currently available for research purposes, but they still fall short for the growing interest in the development of dialogue systems with their own specific requirements. In order to help those requiring such a corpus, this paper surveys a range of available options, in terms of aspects like speakers, size, languages, collection, annotations, and domains. Some trends are identified and possible approaches for the creation of new corpora are also discussed.