Alessandro Sordoni

Philip Bachman

Aaron Courville

Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by red… (see more)ucing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flexible, many-to-many mappings. We propose a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains. We examine Augmented CycleGAN qualitatively and quantitatively on several image datasets.

2018-07-02

Proceedings of the 35th International Conference on Machine Learning (published)

proceedings.mlr.press

Focused Hierarchical RNNs for Conditional Sequence Processing

Nan Rosemary Ke

Adam Trischler

Recurrent Neural Networks (RNNs) with attention mechanisms have obtained state-of-the-art results for many sequence processing tasks. Most o… (see more)f these models use a simple form of encoder with attention that looks over the entire sequence and assigns a weight to each token independently. We present a mechanism for focusing RNN encoders for sequence modelling tasks which allows them to attend to key parts of the input as needed. We formulate this using a multi-layer conditional sequence encoder that reads in one token at a time and makes a discrete decision on whether the token is relevant to the context or question being asked. The discrete gating mechanism takes in the context embedding and the current hidden state as inputs and controls information flow into the layer above. We train it using policy gradient methods. We evaluate this method on several types of tasks with different attributes. First, we evaluate the method on synthetic tasks which allow us to evaluate the model for its generalization ability and probe the behavior of the gates in more controlled settings. We then evaluate this approach on large scale Question Answering tasks including the challenging MS MARCO and SearchQA tasks. Our models shows consistent improvements for both tasks over prior work and our baselines. It has also shown to generalize significantly better on synthetic tasks as compared to the baselines.

2018-07-02

Proceedings of the 35th International Conference on Machine Learning (published)

proceedings.mlr.press

Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences

We propose a hierarchical model for sequential data that learns a tree on-the-fly, i.e. while reading the sequence. In the model, a recurren… (see more)t network adapts its structure and reuses recurrent weights in a recursive manner. This creates adaptive skip-connections that ease the learning of long-term dependencies. The tree structure can either be inferred without supervision through reinforcement learning, or learned in a supervised manner. We provide preliminary experiments in a novel Math Expression Evaluation (MEE) task, which is created to have a hierarchical tree structure that can be used to study the effectiveness of our model. Additionally, we test our model in a well-known propositional logic and language modelling tasks. Experimental results have shown the potential of our approach.

2018-06-30

Rep4NLP@ACL (published)

Straight to the Tree: Constituency Parsing with Neural Syntactic Distance

In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances,… (see more) for each split position in the input sentence. The syntactic distances specify the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Compared to traditional shift-reduce parsing schemes, our approach is free from the potential problem of compounding errors, while being faster and easier to parallelize. Our model achieves competitive performance amongst single model, discriminative parsers in the PTB dataset and outperforms previous models in the CTB dataset.

2018-06-30

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)

Towards Text Generation with Adversarially Learned Neural Outlines.

Sandeep Subramanian

Sai Rajeswar

Adam Trischler

Aaron C. Courville

Chris Pal

2017-12-31

Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (published)

dblp.uni-trier.de

Twin Networks: Matching the Future for Sequence Generation

Dmitriy Serdyuk

Nan Rosemary Ke

Adam Trischler

Christopher Pal

Yoshua Bengio

We propose a simple technique for encouraging generative RNNs to plan ahead. We train a "backward" recurrent network to generate a given seq… (see more)uence in reverse order, and we encourage states of the forward model to predict cotemporal states of the backward model. The backward network is used only during training, and plays no role during sampling or inference. We hypothesize that our approach eases modeling of long-term dependencies by implicitly forcing the forward states to hold information about the longer-term future (as contained in the backward states). We show empirically that our approach achieves 9% relative improvement for a speech recognition task, and achieves significant improvement on a COCO caption generation task.

2017-12-31

ICLR (Poster) (published)

openreview.net

Twin Networks: Using the Future as a Regularizer

Dmitriy Serdyuk

Nan Rosemary Ke

Christopher Pal

Yoshua Bengio

Being able to model long-term dependencies in sequential data, such as text, has been among the long-standing challenges of recurrent neural… (see more) networks (RNNs). This issue is strictly related to the absence of explicit planning in current RNN architectures. More explicitly, the RNNs are trained to predict only the next token given previous ones. In this paper, we introduce a simple way of encouraging the RNNs to plan for the future. In order to accomplish this, we introduce an additional neural network which is trained to generate the sequence in reverse order, and we require closeness between the states of the forward RNN and backward RNN that predict the same token. At each step, the states of the forward RNN are required to match the future information contained in the backward states. We hypothesize that the approach eases modeling of long-term dependencies thus helping in generating more globally consistent samples. The model trained with conditional generation for a speech recognition task achieved 12\% relative improvement (CER of 6.7 compared to a baseline of 7.6).

2017-08-21

ArXiv (preprint)

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Iulian V. Serban

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterance… (see more)s 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.

2017-02-11

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Z-Forcing: Training Stochastic Recurrent Networks

Nan Rosemary Ke

Many efforts have been devoted to training generative latent variable models with autoregressive decoders, such as recurrent neural networks… (see more) (RNN). Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech. We unify successful ideas from recently proposed architectures into a stochastic recurrent model: each step in the sequence is associated with a latent variable that is used to condition the recurrent dynamics for future steps. Training is performed with amortized variational inference where the approximate posterior is augmented with a RNN that runs backward through the sequence. In addition to maximizing the variational lower bound, we ease training of the latent variables by adding an auxiliary cost which forces them to reconstruct the state of the backward recurrent network. This provides the latent variables with a task-independent objective that enhances the performance of the overall model. We found this strategy to perform better than alternative approaches such as KL annealing. Although being conceptually simple, our model achieves state-of-the-art results on standard speech benchmarks such as TIMIT and Blizzard and competitive performance on sequential MNIST. Finally, we apply our model to language modeling on the IMDB dataset where the auxiliary cost helps in learning interpretable latent variables. Source Code: https://github.com/anirudh9119/zforcing_nips17

2016-12-31

Advances in Neural Information Processing Systems 30 (NIPS 2017) (published)

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

Iulian V. Serban

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Ge… (see more)nerative 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.

2016-03-04

Proceedings of the AAAI Conference on Artificial Intelligence (published)