Because "chatbot" today are highly specialized, Google is working on developing an almost universal conversational agent. The goal : ensure it is able to discuss everything and anything. In this year, Meena approximates human.

Des chercheurs de l’équipe Google Brain, spécialisée dans le deep learning (deep learning), viennent de présenter Meena, a chatbot capable de tenir des conversations de manière beaucoup plus naturelle que d’autres agents conversationnels. Ils le qualifient de modèle conversationnel neuronal qui a été entraîné de bout en bout avec 2,6 billion settings.

Meena repose sur l’architecture Evolved Transformer seq2seq, et se compose d’un seul bloc d’encodage qui lui permet de comprendre les phrases de son interlocuteur et le sujet de la discussion, and of 13 decoding blocks to formulate its response. The researchers used 341 Go de texte en provenance de conversations sur les social networks for driving the chatbot. Their goal was to reduce the perplexity, ie the uncertainty in predicting the next word in the conversation.

Le premier bloc d'encodage permet à Meena de cerner le sujet de la discussion. © Google

A score close exchanges between humans

One problem in evaluating the performance of Meena was no reliable metric. Les chercheurs ont donc créé une nouvelle mesure appelée Sensibleness and Specificity Average (IN). To do this, they collected a hundred conversations for each agent, composed of 1.600 at 2.400 tours (or answers). Volunteers then had to rate each response, if it seemed reasonable and credible in the context, et si elle était spécifique au contexte et non une réponse passe-partout comme « oui » ou « moi aussi ». The SSA is the average of the scores on these two parameters.

This measure, Meena a largement battu les autres chatbots, with a score of 79 % against 56 % for the best. He approaches even human, rated 86 %. The researchers also discovered that there is an inverse correlation between perplexity and SSA, meaning that the perplexity could be used to automatically evaluate the performance of conversational agents, to accelerate their development.


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