Lilia, A Showcase for Fast Bootstrap of Conversation-Like Dialogues Based on a Goal-Oriented System

International audienceRecently many works have proposed to cast human-machine interaction in a sentence generation scheme. Neural networks models can learn how to generate a probable sentence based on the user's statement along with a partial view of the dialogue history. While appealing to some extent, these approaches require huge training sets of general-purpose data and lack a principled way to intertwine language generation with information retrieval from back-end resources to fuel the dialogue with actualised and precise knowledge. As a practical alternative, in this paper, we present Lilia, a showcase for fast bootstrap of conversation-like dialogues based on a goal-oriented system. First, a comparison of goal-oriented and conversational system features is led, then a conversion process is described for the fast bootstrap of a new system, finalised with an on-line training of the system's main components. Lilia is dedicated to a chitchat task, where speakers exchange viewpoints on a displayed image while trying collaboratively to derive its author's intention. Evaluations with user trials showed its efficiency in a realistic setup

Homocysteine metabolism pathway is involved in the control of glucose homeostasis: a cystathionine beta synthase deficiency study in mouse

Cystathionine beta synthase (CBS) catalyzes the first step of the transsulfuration pathway from homocysteine to cystathionine, and its deficiency leads to hyperhomocysteinemia (HHcy) in humans and rodents. To date, scarce information is available about the HHcy effect on insulin secretion, and the link between CBS activity and the setting of type 2 diabetes is still unknown. We aimed to decipher the consequences of an inborn defect in CBS on glucose homeostasis in mice. We used a mouse model heterozygous for CBS (CBS+/-) that presented a mild HHcy. Other groups were supplemented with methionine in drinking water to increase the mild to intermediate HHcy, and were submitted to a high-fat diet (HFD). We measured the food intake, body weight gain, body composition, glucose homeostasis, plasma homocysteine level, and CBS activity. We evidenced a defect in the stimulated insulin secretion in CBS+/- mice with mild and intermediate HHcy, while mice with intermediate HHcy under HFD presented an improvement in insulin sensitivity that compensated for the decreased insulin secretion and permitted them to maintain a glucose tolerance similar to the CBS+/+ mice. Islets isolated from CBS+/- mice maintained their ability to respond to the elevated glucose levels, and we showed that a lower parasympathetic tone could, at least in part, be responsible for the insulin secretion defect. Our results emphasize the important role of Hcy metabolic enzymes in insulin secretion and overall glucose homeostasis

Nonstrict hierarchical reinforcement learning for interactive systems and robots

Conversational systems and robots that use reinforcement learning for policy optimization in large domains often face the problem of limited scalability. This problem has been addressed either by using function approximation techniques that estimate the approximate true value function of a policy or by using a hierarchical decomposition of a learning task into subtasks. We present a novel approach for dialogue policy optimization that combines the benefits of both hierarchical control and function approximation and that allows flexible transitions between dialogue subtasks to give human users more control over the dialogue. To this end, each reinforcement learning agent in the hierarchy is extended with a subtask transition function and a dynamic state space to allow flexible switching between subdialogues. In addition, the subtask policies are represented with linear function approximation in order to generalize the decision making to situations unseen in training. Our proposed approach is evaluated in an interactive conversational robot that learns to play quiz games. Experimental results, using simulation and real users, provide evidence that our proposed approach can lead to more flexible (natural) interactions than strict hierarchical control and that it is preferred by human users

Compact and Interpretable Dialogue State Representation with Genetic Sparse Distributed Memory

International audiencet User satisfaction is often considered as the objective that should be achieved by spoken dialogue systems. This is why, the reward function of Spoken Dialogue Systems (SDS) trained by Reinforcement Learning (RL) is often designed to reflect user satisfaction. To do so, the state space representation should be based on features capturing user satisfaction characteristics such as the mean speech recognition confidence score for instance. On the other hand, for deployment in industrial systems, there is a need for state representations that are understandable by system engineers. In this paper, we propose to represent the state space using a Genetic Sparse Distributed Memory. This is a state aggregation method computing state prototypes which are selected so as to lead to the best linear representation of the value function in RL. To do so, previous work on Genetic Sparse Distributed Memory for classification is adapted to the Reinforcement Learning task and a new way of building the prototypes is proposed. The approach is tested on a corpus of dialogues collected with an appointment scheduling system. The results are compared to a grid-based linear parametrisation. It is shown that learning is accelerated and made more memory efficient. It is also shown that the framework is calable in that it is possible to include many dialogue features in the representation, interpret the resulting policy and identify the most important dialogue features

Uncertainty management for on-line optimisation of a POMDP-based large-scale spoken dialogue system

The optimization of dialogue policies using reinforcement learning (RL) is now an accepted part of the state of the art in spoken dialogue systems (SDS). Yet, it is still the case that the commonly used training algorithms for SDS require a large number of dialogues and hence most systems still rely on artificial data generated by a user simulator. Optimization is therefore performed off-line before releasing the system to real users. Gaussian Processes (GP) for RL have recently been applied to dialogue systems. One advantage of GP is that they compute an explicit measure of uncertainty in the value function estimates computed during learning. In this paper, a class of novel learning strategies is described which use uncertainty to control exploration on-line. Comparisons between several exploration schemes show that significant improvements to learning speed can be obtained and that rapid and safe online optimisation is possible, even on a complex task. Copyright © 2011 ISCA

Simulating Human-Robot Interactions for Dialogue Strategy Learning

International audienceMany robotic projects use simulation as a faster and easier way to develop, evaluate and validate software components compared with on-board real world settings. In the human-robot interaction field, some recent works have attempted to integrate humans in the simulation loop. In this paper we investigate how such kind of robotic simulation software can be used to provide a dynamic and interactive environment to both collect a multimodal situated dialogue corpus and to perform an efficient reinforcement learning-based dialogue management optimisation procedure. Our proposition is illustrated by a preliminary experiment involving real users in a Pick-Place-Carry task for which encouraging results are obtained

Alterations in the Serotonin and Dopamine Pathways by Cystathionine Beta Synthase Overexpression in Murine Brain

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LPS-Induced Inflammation Abolishes the Effect of DYRK1A on IkB Stability in the Brain of Mice

This work was supported by the Fondation Jerome Lejeune (grant number 2017a-1634)