Melina Alberio Guerra

Claudio Pinhanez, Paulo Cavalin, Victor Ribeiro et al.

In this paper we explore the use of meta-knowledge embedded in intent identifiers to improve intent recognition in conversational systems. As evidenced by the analysis of thousands of real-world chatbots and in interviews with professional chatbot curators, developers and domain experts tend to organize the set of chatbot intents by identifying them using proto-taxonomies, i.e., meta-knowledge connecting high-level, symbolic concepts shared across different intents. By using neuro-symbolic algorithms able to incorporate such proto-taxonomies to expand intent representation, we show that such mined meta-knowledge can improve accuracy in intent recognition. In a dataset with intents and example utterances from hundreds of professional chatbots, we saw improvements of more than 10% in the equal error rate (EER) in almost a third of the chatbots when we apply those algorithms in comparison to a baseline of the same algorithms without the meta-knowledge. The meta-knowledge proved to be even more relevant in detecting out-of-scope utterances, decreasing the false acceptance rate (FAR) in more than 20\% in about half of the chatbots. The experiments demonstrate that such symbolic meta-knowledge structures can be effectively mined and used by neuro-symbolic algorithms, apparently by incorporating into the learning process higher-level structures of the problem being solved. Based on these results, we also discuss how the use of mined meta-knowledge can be an answer for the challenge of knowledge acquisition in neuro-symbolic algorithms.

Maira Gatti de Bayser, Melina Alberio Guerra, Paulo Cavalin et al.

To predict the next most likely participant to interact in a multi-party conversation is a difficult problem. In a text-based chat group, the only information available is the sender, the content of the text and the dialogue history. In this paper we present our study on how these information can be used on the prediction task through a corpus and architecture that integrates turn-taking classifiers based on Maximum Likelihood Expectation (MLE), Convolutional Neural Networks (CNN) and Finite State Automata (FSA). The corpus is a synthetic adaptation of the Multi-Domain Wizard-of-Oz dataset (MultiWOZ) to a multiple travel service-based bots scenario with dialogue errors and was created to simulate user's interaction and evaluate the architecture. We present experimental results which show that the CNN approach achieves better performance than the baseline with an accuracy of 92.34%, but the integrated solution with MLE, CNN and FSA achieves performance even better, with 95.65%.

Claudio S. Pinhanez, Heloisa Candello, Mauro C. Pichiliani et al.

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.