Annotating Errors and Emotions in Human-Chatbot Interactions in Italian

This paper describes a novel annotation scheme specifically designed for a customer-service context where written interactions take place between a given user and the chatbot of an Italian telecommunication company. More specifically, the scheme aims to detect and highlight two aspects: the presence of errors in the conversation on both sides (i.e. customer and chatbot) and the “emotional load” of the conversation. This can be inferred from the presence of emotions of some kind (especially negative ones) in the customer messages, and from the possible empathic responses provided by the agent. The dataset annotated according to this scheme is currently used to develop the prototype of a rule-based Natural Language Generation system aimed at improving the chatbot responses and the customer experience overall

Lessons Learned from EVALITA 2020 and Thirteen Years of Evaluation of Italian Language Technology

This paper provides a summary of the 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA2020) which was held online on December 17th, due to the 2020 COVID-19 pandemic. The 2020 edition of Evalita included 14 different tasks belonging to five research areas, namely: (i) Affect, Hate, and Stance, (ii) Creativity and Style, (iii) New Challenges in Long-standing Tasks, (iv) Semantics and Multimodality, (v) Time and Diachrony. This paper provides a description of the tasks and the key findings from the analysis of participant outcomes. Moreover, it provides a detailed analysis of the participants and task organizers which demonstrates the growing interest with respect to this campaign. Finally, a detailed analysis of the evaluation of tasks across the past seven editions is provided; this allows to assess how the research carried out by the Italian community dealing with Computational Linguistics has evolved in terms of popular tasks and paradigms during the last 13 years