Representation learning for dialogue systems

Cette thèse présente une série de mesures prises pour étudier l’apprentissage de représentations (par exemple, l’apprentissage profond) afin de mettre en place des systèmes de dialogue et des agents de conversation virtuels. La thèse est divisée en deux parties générales. La première partie de la thèse examine l’apprentissage des représentations pour les modèles de dialogue génératifs. Conditionnés sur une séquence de tours à partir d’un dialogue textuel, ces modèles ont la tâche de générer la prochaine réponse appropriée dans le dialogue. Cette partie de la thèse porte sur les modèles séquence-à-séquence, qui est une classe de réseaux de neurones profonds génératifs. Premièrement, nous proposons un modèle d’encodeur-décodeur récurrent hiérarchique ("Hierarchical Recurrent Encoder-Decoder"), qui est une extension du modèle séquence-à-séquence traditionnel incorporant la structure des tours de dialogue. Deuxièmement, nous proposons un modèle de réseau de neurones récurrents multi-résolution ("Multiresolution Recurrent Neural Network"), qui est un modèle empilé séquence-à-séquence avec une représentation stochastique intermédiaire (une "représentation grossière") capturant le contenu sémantique abstrait communiqué entre les locuteurs. Troisièmement, nous proposons le modèle d’encodeur-décodeur récurrent avec variables latentes ("Latent Variable Recurrent Encoder-Decoder"), qui suivent une distribution normale. Les variables latentes sont destinées à la modélisation de l’ambiguïté et l’incertitude qui apparaissent naturellement dans la communication humaine. Les trois modèles sont évalués et comparés sur deux tâches de génération de réponse de dialogue: une tâche de génération de réponses sur la plateforme Twitter et une tâche de génération de réponses de l’assistance technique ("Ubuntu technical response generation task"). La deuxième partie de la thèse étudie l’apprentissage de représentations pour un système de dialogue utilisant l’apprentissage par renforcement dans un contexte réel. Cette partie porte plus particulièrement sur le système "Milabot" construit par l’Institut québécois d’intelligence artificielle (Mila) pour le concours "Amazon Alexa Prize 2017". Le Milabot est un système capable de bavarder avec des humains sur des sujets populaires à la fois par la parole et par le texte. Le système consiste d’un ensemble de modèles de récupération et de génération en langage naturel, comprenant des modèles basés sur des références, des modèles de sac de mots et des variantes des modèles décrits ci-dessus. Cette partie de la thèse se concentre sur la tâche de sélection de réponse. À partir d’une séquence de tours de dialogues et d’un ensemble des réponses possibles, le système doit sélectionner une réponse appropriée à fournir à l’utilisateur. Une approche d’apprentissage par renforcement basée sur un modèle appelée "Bottleneck Simulator" est proposée pour sélectionner le candidat approprié pour la réponse. Le "Bottleneck Simulator" apprend un modèle approximatif de l’environnement en se basant sur les trajectoires de dialogue observées et le "crowdsourcing", tout en utilisant un état abstrait représentant la sémantique du discours. Le modèle d’environnement est ensuite utilisé pour apprendre une stratégie d’apprentissage du renforcement par le biais de simulations. La stratégie apprise a été évaluée et comparée à des approches concurrentes via des tests A / B avec des utilisateurs réel, où elle démontre d’excellente performance.This thesis presents a series of steps taken towards investigating representation learning (e.g. deep learning) for building dialogue systems and conversational agents. The thesis is split into two general parts. The first part of the thesis investigates representation learning for generative dialogue models. Conditioned on a sequence of turns from a text-based dialogue, these models are tasked with generating the next, appropriate response in the dialogue. This part of the thesis focuses on sequence-to-sequence models, a class of generative deep neural networks. First, we propose the Hierarchical Recurrent Encoder-Decoder model, which is an extension of the vanilla sequence-to sequence model incorporating the turn-taking structure of dialogues. Second, we propose the Multiresolution Recurrent Neural Network model, which is a stacked sequence-to-sequence model with an intermediate, stochastic representation (a "coarse representation") capturing the abstract semantic content communicated between the dialogue speakers. Third, we propose the Latent Variable Recurrent Encoder-Decoder model, which is a variant of the Hierarchical Recurrent Encoder-Decoder model with latent, stochastic normally-distributed variables. The latent, stochastic variables are intended for modelling the ambiguity and uncertainty occurring naturally in human language communication. The three models are evaluated and compared on two dialogue response generation tasks: a Twitter response generation task and the Ubuntu technical response generation task. The second part of the thesis investigates representation learning for a real-world reinforcement learning dialogue system. Specifically, this part focuses on the Milabot system built by the Quebec Artificial Intelligence Institute (Mila) for the Amazon Alexa Prize 2017 competition. Milabot is a system capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language retrieval and generation models, including template-based models, bag-of-words models, and variants of the models discussed in the first part of the thesis. This part of the thesis focuses on the response selection task. Given a sequence of turns from a dialogue and a set of candidate responses, the system must select an appropriate response to give the user. A model-based reinforcement learning approach, called the Bottleneck Simulator, is proposed for selecting the appropriate candidate response. The Bottleneck Simulator learns an approximate model of the environment based on observed dialogue trajectories and human crowdsourcing, while utilizing an abstract (bottleneck) state representing high-level discourse semantics. The learned environment model is then employed to learn a reinforcement learning policy through rollout simulations. The learned policy has been evaluated and compared to competing approaches through A/B testing with real-world users, where it was found to yield excellent performance

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the high-level coarse tokens, but we argue that a simple extraction procedure is sufficient to capture a wealth of high-level discourse semantics. Such procedure allows training the multiresolution recurrent neural network by maximizing the exact joint log-likelihood over both sequences. In contrast to the standard log- likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions. We apply the proposed model to the task of dialogue response generation in two challenging domains: the Ubuntu technical support domain, and Twitter conversations. On Ubuntu, the model outperforms competing approaches by a substantial margin, achieving state-of-the-art results according to both automatic evaluation metrics and a human evaluation study. On Twitter, the model appears to generate more relevant and on-topic responses according to automatic evaluation metrics. Finally, our experiments demonstrate that the proposed model is more adept at overcoming the sparsity of natural language and is better able to capture long-term structure.Comment: 21 pages, 2 figures, 10 table

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue. In an effort to model this kind of generative process, we propose a neural network-based generative architecture, with latent stochastic variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with recent neural network architectures. We evaluate the model performance through automatic evaluation metrics and by carrying out a human evaluation. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate the generation of long outputs and maintain the context.Comment: 15 pages, 5 tables, 4 figure

A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective

Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.Comment: 13 pages, 1 figure, 7 table

Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus

In this paper, we construct and train end-to-end neural network-based dialogue systems usingan updated version of the recent Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This dataset is interesting because of its size, long context lengths, and technical nature; thus, it can be used to train large models directly from data with minimal feature engineering, which can be both time consuming and expensive. We provide baselines  in two different environments: one where models are trained to maximize the log-likelihood of a generated utterance  conditioned on the context of the conversation, and one where models are trained to select the correct next response from a list of candidate responses. These are both evaluated on a recall task that we call Next Utterance Classification (NUC), as well as other generation-specific metrics. Finally, we provide a qualitative error analysis to help determine the most promising directions for future research on the Ubuntu  Dialogue Corpus, and for end-to-end dialogue systems in general