Models, code, and papers for "Heloisa Candello":

A Hybrid Architecture for Multi-Party Conversational Systems

May 04, 2017
Maira Gatti de Bayser, Paulo Cavalin, Renan Souza, Alan Braz, Heloisa Candello, Claudio Pinhanez, Jean-Pierre Briot

Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems. From the moment that an utterance is sent to a group, to the moment that it is replied in the group by a member, several activities must be done by the system: utterance understanding, information search, reasoning, among others. In this paper we present the challenges of designing and building multi-party conversational systems, the state of the art, our proposed hybrid architecture using both rules and machine learning and some insights after implementing and evaluating one on the finance domain.

  Click for Model/Code and Paper
Different but Equal: Comparing User Collaboration with Digital Personal Assistants vs. Teams of Expert Agents

Aug 24, 2018
Claudio S. Pinhanez, Heloisa Candello, Mauro C. Pichiliani, Marisa Vasconcelos, Melina Guerra, Maíra G. de Bayser, Paulo Cavalin

This work compares user collaboration with conversational personal assistants vs. teams of expert chatbots. Two studies were performed to investigate whether each approach affects accomplishment of tasks and collaboration costs. Participants interacted with two equivalent financial advice chatbot systems, one composed of a single conversational adviser and the other based on a team of four experts chatbots. Results indicated that users had different forms of experiences but were equally able to achieve their goals. Contrary to the expected, there were evidences that in the teamwork situation that users were more able to predict agent behavior better and did not have an overhead to maintain common ground, indicating similar collaboration costs. The results point towards the feasibility of either of the two approaches for user collaboration with conversational agents.

  Click for Model/Code and Paper