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

Progettare chatbot: considerazioni e linee guida

Il lavoro si propone di delineare una serie di linee guida per la progettazione di chatbot e assistenti virtuali a partire dall’analisi degli attuali trend di progettazione e delle esigenze lato utente rilevate da precedenti lavori di rassegna della letteratura esistente. Il presente lavoro è stato svolto nell’ambito del progetto “Cognitive Solution for Intelligent Caring” di TIM.This work is focused on the current trends in designing chatbots and virtual assistants. We start from users’ needs identified in industrial surveys on chatbots. The result is a collection of guidelines and considerations which reflect the state of the art

Anticipating User Intentions in Customer Care Dialogue Systems

In this article, we investigate the case of human-machine dialogues in the specific domain of commercial customer care. We built a corpus of conversations between users and a customer-care chatbot of an Italian Telecom Company, focusing on a sample of conversations where users contact the service asking for explanations about billing issues or overcharges. We observed that users' requests are often vague, generic or incomprehensible. In such cases, commercial dialogue systems typically ask for clarifications or further details to fully understand users' specific requests. However, from the corpus analysis it appeared that chatbot's clarifying requests may result in ineffective interactions, with users eventually giving up the conversation or switching to a human agent for a faster query resolution. A recovery strategy is thus needed to anticipate users' information needs, or intentions. We address this issue resorting to GEN-DS, a dialogue system based on symbolic data-to-text generation. GEN-DS analyzes the user-company contextual relational knowledge, with the aim to generate more relevant answers to unclear questions. In this article, we describe the GEN-DS architecture along with the experiments we carried out to evaluate its output. Results from an offline human evaluation show significant improvements of GEN-DS compared to the original system. These improvements concern properties such as utility, necessity, understandability, and quickness of the information communicated in the dialogue. We believe that GEN-DS techniques may find application in all the dialogue systems that need to manage vague requests and must rely on relational knowledge

The art of video MashUp: supporting creative users with an innovative and smart application

In this paper, we describe the development of a new and innovative tool of video mashup. This application is an easy to use tool of video editing integrated in a cross-media platform; it works taking the information from a repository of videos and puts into action a process of semi-automatic editing supporting users in the production of video mashup. Doing so it gives vent to their creative side without them being forced to learn how to use a complicated and unlikely new technology. The users will be further helped in building their own editing by the intelligent system working behind the tool: it combines semantic annotation (tags and comments by users), low level features (gradient of color, texture and movements) and high level features (general data distinguishing a movie: actors, director, year of production, etc.) to furnish a pre-elaborated editing users can modify in a very simple way

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges