451 research outputs found
Consistent Goal-Directed User Model for Realistic Man-Machine Task-Oriented Spoken Dialogue Simulation
International audienceBecause of the great variability of factors to take into account, designing a spoken dialogue system is still a tailoring task. Rapid design and reusability of previous work is made very difficult. For these reasons, the application of machine learning methods to dia-logue strategy optimization has become a leading subject of re-searches this last decade. Yet, techniques such as reinforcement learning are very demanding in training data while obtaining a substantial amount of data in the particular case of spoken dia-logues is time-consuming and therefore expansive. In order to expand existing data sets, dialogue simulation techniques are be-coming a standard solution. In this paper we describe a user modeling technique for realis-tic simulation of man-machine goal-directed spoken dialogues. This model, based on a stochastic description of man-machine communication, unlike previously proposed models, is consistent along the interaction according to its history and a predefined user goal
Learning to ground in spoken dialogue systems
PosterMachine learning methods such as reinforcement learning applied to dialogue strategy optimization has become a leading subject of researches since the mid 90's. Indeed, the great variability of factors to take into account makes the design of a spoken dialogue system a tailoring task and reusability of previous work is very difficult. Yet, techniques such as reinforcement learning are very demanding in training data while obtaining a substantial amount of data in the particular case of spoken dialogues is time-consuming and therefore expansive. In order to expand existing data sets, dialogue simulation techniques are becoming a standard solution. In this paper, we present a user model for realistic spoken dialogue simulation and a method for using this model so as to simulate the grounding process. This allows including grounding subdialogues as actions in the reinforcement learning process and learning adapted strateg
Optimising Spoken Dialogue Strategies within the Reinforcement Learning Paradigm
Optimising Spoken Dialogue Strategies within the Reinforcement Learning Paradig
Un Cadre Probabiliste pour l'Optimisation des Systèmes de Dialogue
Dans cet article, un cadre théorique pour la simulation et l'optimisation automatique de systèmes de dialogues vocaux entre homme et machine par le biais d'un apprentissage non-supervisé de stratégies est proposé. Ce cadre s'appuie sur une description probabiliste de la communication parlée entre homme et machine. Il permet de s'inscrire dans le cadre des processus décisionnels de Markov et de faire usage de l'apprentissage par renforcement pour rechercher une stratégie optimale de manière indépendante de la tâche. Deux applications concrètes du cadre proposé aux cas du remplissage de formulaire et de l'interrogation de bases de données sont données afin d'en démontrer les utilisations possibles
Is the Bellman residual a bad proxy?
This paper aims at theoretically and empirically comparing two standard
optimization criteria for Reinforcement Learning: i) maximization of the mean
value and ii) minimization of the Bellman residual. For that purpose, we place
ourselves in the framework of policy search algorithms, that are usually
designed to maximize the mean value, and derive a method that minimizes the
residual over policies. A theoretical analysis
shows how good this proxy is to policy optimization, and notably that it is
better than its value-based counterpart. We also propose experiments on
randomly generated generic Markov decision processes, specifically designed for
studying the influence of the involved concentrability coefficient. They show
that the Bellman residual is generally a bad proxy to policy optimization and
that directly maximizing the mean value is much better, despite the current
lack of deep theoretical analysis. This might seem obvious, as directly
addressing the problem of interest is usually better, but given the prevalence
of (projected) Bellman residual minimization in value-based reinforcement
learning, we believe that this question is worth to be considered.Comment: Final NIPS 2017 version (title, among other things, changed
A Theory of Regularized Markov Decision Processes
Many recent successful (deep) reinforcement learning algorithms make use of
regularization, generally based on entropy or Kullback-Leibler divergence. We
propose a general theory of regularized Markov Decision Processes that
generalizes these approaches in two directions: we consider a larger class of
regularizers, and we consider the general modified policy iteration approach,
encompassing both policy iteration and value iteration. The core building
blocks of this theory are a notion of regularized Bellman operator and the
Legendre-Fenchel transform, a classical tool of convex optimization. This
approach allows for error propagation analyses of general algorithmic schemes
of which (possibly variants of) classical algorithms such as Trust Region
Policy Optimization, Soft Q-learning, Stochastic Actor Critic or Dynamic Policy
Programming are special cases. This also draws connections to proximal convex
optimization, especially to Mirror Descent.Comment: ICML 201
Difference of Convex Functions Programming Applied to Control with Expert Data
This paper reports applications of Difference of Convex functions (DC)
programming to Learning from Demonstrations (LfD) and Reinforcement Learning
(RL) with expert data. This is made possible because the norm of the Optimal
Bellman Residual (OBR), which is at the heart of many RL and LfD algorithms, is
DC. Improvement in performance is demonstrated on two specific algorithms,
namely Reward-regularized Classification for Apprenticeship Learning (RCAL) and
Reinforcement Learning with Expert Demonstrations (RLED), through experiments
on generic Markov Decision Processes (MDP), called Garnets
Machine Learning Methods for Spoken Dialogue Simulation and Optimization
Computers and electronic devices are becoming more and more present in our day-to-day life. This can of course be partly explained by their ability to ease the achievement of complex and boring tasks, the important decrease of prices or the new entertainment styles they offer. Yet, this real incursion in everybody's life would not have been possible without an important improvement of Human-Computer Interfaces (HCI). This is why HCI are now widely studied and become a major trend of research among the scientific community. Designing “user-friendly” interfaces usually requires multidisciplinary skills in fields such as computer science, ergonomics, psychology, signal processing etc. In this chapter, we argue that machine learning methods can help in designing efficient speech-based humancomputer interfaces
LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task
This paper presents the LIG-CRIStAL submission to the shared Automatic Post-
Editing task of WMT 2017. We propose two neural post-editing models: a
monosource model with a task-specific attention mechanism, which performs
particularly well in a low-resource scenario; and a chained architecture which
makes use of the source sentence to provide extra context. This latter
architecture manages to slightly improve our results when more training data is
available. We present and discuss our results on two datasets (en-de and de-en)
that are made available for the task.Comment: keywords: neural post-edition, attention model
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