End-to-End Goal-Oriented Conversational Agent for Risk Awareness

Canducci, Marco (2020) End-to-End Goal-Oriented Conversational Agent for Risk Awareness. [Laurea magistrale], Università di Bologna, Corso di Studio in Ingegneria e scienze informatiche [LM-DM270] - Cesena
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Traditional development of goal-oriented conversational agents typically require a lot of domain-specific handcrafting, which precludes scaling up to different domains; end-to-end systems would escape this limitation because they can be trained directly from dialogues. The very promising success recently obtained in end-to-end chatbots development could carry over to goal-oriented settings: applying deep learning models for building robust and scalable goal-oriented dialog systems directly from corpora of conversations is a challenging task and an open research area. For this reason, I decided that it would have been more relevant in the context of a master's thesis to experiment and get acquainted with these new promising methodologies - although not yet ready for production - rather than investing time in hand-crafting dialogue rules for a domain-specific solution. My thesis work had the following macro objectives: (i) investigate the latest research works concerning goal-oriented conversational agents development; (ii) choose a reference study, understand it and implement it with an appropriate technology; (iii) apply what learnt to a particular domain of interest. As a reference framework I chose the end-to-end memory networks (MemN2N) (Sukhbaatar et al., 2015) because it has proven to be particularly promising and has been used as a baseline for many recent works. Not having real dialogues available for training though, I took care of synthetically generating a corpora of conversations, taking a cue from the Dialog bAbI dataset for restaurant reservations (Bordes et al., 2016) and adapting it to the new domain of interest of risk awareness. Finally, I built a simple prototype which exploited the pre-trained dialog model in order to advise users about risk through an anthropomorphic talking avatar interface.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Canducci, Marco
Relatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Goal-Oriented,Task-Oriented,End-to-End,Dialog Systems,Conversational Agents,Memory Networks,Deep Learning,NLP
Data di discussione della Tesi
19 Marzo 2020

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