Models, code, and papers for "Claudio S. Pinhanez":

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
Learning Multi-Party Turn-Taking Models from Dialogue Logs

Jul 03, 2019
Maira Gatti de Bayser, Paulo Cavalin, Claudio Pinhanez, Bianca Zadrozny

This paper investigates the application of machine learning (ML) techniques to enable intelligent systems to learn multi-party turn-taking models from dialogue logs. The specific ML task consists of determining who speaks next, after each utterance of a dialogue, given who has spoken and what was said in the previous utterances. With this goal, this paper presents comparisons of the accuracy of different ML techniques such as Maximum Likelihood Estimation (MLE), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) architectures, with and without utterance data. We present three corpora: the first with dialogues from an American TV situated comedy (chit-chat), the second with logs from a financial advice multi-bot system and the third with a corpus created from the Multi-Domain Wizard-of-Oz dataset (both are topic-oriented). The results show: (i) the size of the corpus has a very positive impact on the accuracy for the content-based deep learning approaches and those models perform best in the larger datasets; and (ii) if the dialogue dataset is small and topic-oriented (but with few topics), it is sufficient to use an agent-only MLE or SVM models, although slightly higher accuracies can be achieved with the use of the content of the utterances with a CNN model.

  Click for Model/Code and Paper
Combining Textual Content and Structure to Improve Dialog Similarity

Feb 20, 2018
Ana Paula Appel, Paulo Rodrigo Cavalin, Marisa Affonso Vasconcelos, Claudio Santos Pinhanez

Chatbots, taking advantage of the success of the messaging apps and recent advances in Artificial Intelligence, have become very popular, from helping business to improve customer services to chatting to users for the sake of conversation and engagement (celebrity or personal bots). However, developing and improving a chatbot requires understanding their data generated by its users. Dialog data has a different nature of a simple question and answering interaction, in which context and temporal properties (turn order) creates a different understanding of such data. In this paper, we propose a novelty metric to compute dialogs' similarity based not only on the text content but also on the information related to the dialog structure. Our experimental results performed over the Switchboard dataset show that using evidence from both textual content and the dialog structure leads to more accurate results than using each measure in isolation.

* 5 pages 

  Click for Model/Code and Paper
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