Unfolding and Shrinking Neural Machine Translation Ensembles

Ensembling is a well-known technique in neural machine translation (NMT) to improve system performance. Instead of a single neural net, multiple neural nets with the same topology are trained separately, and the decoder generates predictions by averaging over the individual models. Ensembling often improves the quality of the generated translations drastically. However, it is not suitable for production systems because it is cumbersome and slow. This work aims to reduce the runtime to be on par with a single system without compromising the translation quality. First, we show that the ensemble can be unfolded into a single large neural network which imitates the output of the ensemble system. We show that unfolding can already improve the runtime in practice since more work can be done on the GPU. We proceed by describing a set of techniques to shrink the unfolded network by reducing the dimensionality of layers. On Japanese-English we report that the resulting network has the size and decoding speed of a single NMT network but performs on the level of a 3-ensemble system.Comment: Accepted at EMNLP 201

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.Comment: 8 pages with references; Published in AAAI 2016 (Special Track on Cognitive Systems

Sequence to sequence learning and its speech applications

Recurrent Neural Networks (RNNs), which has the attractive properties of modelling sequences, has been dominant in speech field in the recent decades. Convolutional Neural Networks (CNNs) has been shown as an alternative to model sequences because of its capacity of reducing spectral variations and modeling spectral correlations in acoustic features for automatic speech recognition (ASR). Recent work suggests that complex numbers could be used as a richer feature representation than spectrum which may benefit the speech related tasks. In the thesis, we first cover the basic concepts in machine learning, building blocks of deep learning and discuss the popular methods that are capable of doing sequence-to-sequence modelling, specially convolutional neural networks, which is famous as a class of feed-forward nets. We then present two research work related to sequence-to-sequence modelling on speech. We introduce a new approach to address speech recognition with convolutional neural networks which shows the comparable results with their recurrent neural networks counterpart. In addition, we present a new model taking advantage of the representation in the complex domain and define complex convolutions, complex batch-normalization, complex weight initialization strategies. The new model results in state-of-the-art of speech spectrum prediction in a convolutional recurrent setting.Les réseaux neuronaux récurrents (RNN) ont été dominants dans le domaine de la parole au cours des dernières décennies, étant donné leurs propriétés attrayantes de modélisation de séquence. Les réseaux neuronaux convolutionnels (CNN) ont été présentés comme une alternative pour la modélisation de séquences en raison de leur capacité à réduire les variations spectrales et à modéliser les corrélations spectrales dans les caractéristiques acoustiques pour la reconnaissance automatique de la parole (ASR). Des travaux récents suggèrent que les nombres complexes pourraient être utilisés comme une représentation de caractéristique plus riche que le spectre et qui pouvaient donc être bénéfique pour les tâches liées à la parole. Dans la thèse, nous abordons d’abord les concepts de base de l’apprentissage automatique, les blocs de construction de l’apprentissage profond et discutons des méthodes populaires capables de faire des modélisations séquentielles, en particulier des réseaux de neurones convolutionnels, célèbres en tant que réseaux feedfoward. Nous présentons ensuite deux travaux de recherche liés à la modélisation séquence-séquence sur la parole. Premierement, nous introduisons une nouvelle approche pour adresser la reconnaissance de la parole avec des réseaux de neurones convolutionnels qui montre des performances comparables avec leur homologue des réseaux neuronaux récurrents. Deuxièmement, nous présentons un nouveau mo- dèle, tirant parti de la représentation dans le domaine complexe, et définissons des circonvolutions complexes, des stratégies complexes de normalisation par lots et d’initialisation de poids complexes. Le modèle a atteint l’état de l’art de la tâche de prédiction du spectre de la parole dans un cadre récurrent convolutionnel