Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators

Natural language generators for task-oriented dialogue must effectively realize system dialogue actions and their associated semantics. In many applications, it is also desirable for generators to control the style of an utterance. To date, work on task-oriented neural generation has primarily focused on semantic fidelity rather than achieving stylistic goals, while work on style has been done in contexts where it is difficult to measure content preservation. Here we present three different sequence-to-sequence models and carefully test how well they disentangle content and style. We use a statistical generator, Personage, to synthesize a new corpus of over 88,000 restaurant domain utterances whose style varies according to models of personality, giving us total control over both the semantic content and the stylistic variation in the training data. We then vary the amount of explicit stylistic supervision given to the three models. We show that our most explicit model can simultaneously achieve high fidelity to both semantic and stylistic goals: this model adds a context vector of 36 stylistic parameters as input to the hidden state of the encoder at each time step, showing the benefits of explicit stylistic supervision, even when the amount of training data is large.Comment: To appear at SIGDIAL 201

Four Mode Based Dialogue Management with Modified POMDP Model

This thesis proposes a method to manage the interaction between the user and the system dynamically, through speech or text input which updates the user goals, select system actions and calculate rewards for each system response at each time-stamp. The main focus is made on the dialog manager, which decides how to continue the dialogue. We have used POMDP technique, as it maintains a belief distribution on the dialogue states based on the observations over the dialogue even in a noisy environment. Four contextual control modes are introduced in dialogue management for decision-making mechanism, and to keep track of machine behaviour for each dialogue state. The result obtained proves that our proposed framework has overcome the limitations of prior POMDP methods, and exactly understands the actual intention of the users within the available time, providing very interactive conversation between the user and the computer

Learning dialogue POMDP model components from expert dialogues

Un système de dialogue conversationnel doit aider les utilisateurs humains à atteindre leurs objectifs à travers des dialogues naturels et efficients. C'est une tache toutefois difficile car les langages naturels sont ambiguës et incertains, de plus le système de reconnaissance vocale (ASR) est bruité. À cela s'ajoute le fait que l'utilisateur humain peut changer son intention lors de l'interaction avec la machine. Dans ce contexte, l'application des processus décisionnels de Markov partiellement observables (POMDPs) au système de dialogue conversationnel nous a permis d'avoir un cadre formel pour représenter explicitement les incertitudes, et automatiser la politique d'optimisation. L'estimation des composantes du modelé d'un POMDP-dialogue constitue donc un défi important, car une telle estimation a un impact direct sur la politique d'optimisation du POMDP-dialogue. Cette thèse propose des méthodes d'apprentissage des composantes d'un POMDPdialogue basées sur des dialogues bruités et sans annotation. Pour cela, nous présentons des méthodes pour apprendre les intentions possibles des utilisateurs à partir des dialogues, en vue de les utiliser comme états du POMDP-dialogue, et l'apprendre un modèle du maximum de vraisemblance à partir des données, pour transition du POMDP. Car c'est crucial de réduire la taille d'état d'observation, nous proposons également deux modèles d'observation: le modelé mot-clé et le modelé intention. Dans les deux modèles, le nombre d'observations est réduit significativement tandis que le rendement reste élevé, particulièrement dans le modele d'observation intention. En plus de ces composantes du modèle, les POMDPs exigent également une fonction de récompense. Donc, nous proposons de nouveaux algorithmes pour l'apprentissage du modele de récompenses, un apprentissage qui est basé sur le renforcement inverse (IRL). En particulier, nous proposons POMDP-IRL-BT qui fonctionne sur les états de croyance disponibles dans les dialogues du corpus. L'algorithme apprend le modele de récompense par l'estimation du modele de transition de croyance, semblable aux modèles de transition des états dans un MDP (processus décisionnel de Markov). Finalement, nous appliquons les méthodes proposées à un domaine de la santé en vue d'apprendre un POMDP-dialogue et ce essentiellement à partir de dialogues réels, bruités, et sans annotations.Spoken dialogue systems should realize the user intentions and maintain a natural and efficient dialogue with users. This is however a difficult task as spoken language is naturally ambiguous and uncertain, and further the automatic speech recognition (ASR) output is noisy. In addition, the human user may change his intention during the interaction with the machine. To tackle this difficult task, the partially observable Markov decision process (POMDP) framework has been applied in dialogue systems as a formal framework to represent uncertainty explicitly while supporting automated policy solving. In this context, estimating the dialogue POMDP model components is a signifficant challenge as they have a direct impact on the optimized dialogue POMDP policy. This thesis proposes methods for learning dialogue POMDP model components using noisy and unannotated dialogues. Speciffically, we introduce techniques to learn the set of possible user intentions from dialogues, use them as the dialogue POMDP states, and learn a maximum likelihood POMDP transition model from data. Since it is crucial to reduce the observation state size, we then propose two observation models: the keyword model and the intention model. Using these two models, the number of observations is reduced signifficantly while the POMDP performance remains high particularly in the intention POMDP. In addition to these model components, POMDPs also require a reward function. So, we propose new algorithms for learning the POMDP reward model from dialogues based on inverse reinforcement learning (IRL). In particular, we propose the POMDP-IRL-BT algorithm (BT for belief transition) that works on the belief states available in the dialogues. This algorithm learns the reward model by estimating a belief transition model, similar to MDP (Markov decision process) transition models. Ultimately, we apply the proposed methods on a healthcare domain and learn a dialogue POMDP essentially from real unannotated and noisy dialogues