200 citations · 728 across the 14 of their papers we have counts for
15 papers
Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation
Virginia Adams, Sandeep Subramanian, Mike Chrzanowski +2
General translation models often still struggle to generate accurate translations in specialized domains. To guide machine translation practitioners and characterize the effectiven…
NVIDIA NeMo Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21
Sandeep Subramanian, Oleksii Hrinchuk, Virginia Adams +1
This paper provides an overview of NVIDIA NeMo's neural machine translation systems for the constrained data track of the WMT21 News and Biomedical Shared Translation Tasks. Our ne…
Multi-scale Transformer Language Models
Sandeep Subramanian, Ronan Collobert, Marc'Aurelio Ranzato +1
We investigate multi-scale transformer language models that learn representations of text at multiple scales, and present three different architectures that have an inductive bias…
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
Sandeep Subramanian, Raymond Li, Jonathan Pilault +1
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. We perform a simple extractive step…
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal +5
Machine learning promises methods that generalize well from finite labeled data. However, the brittleness of existing neural net approaches is revealed by notable failures, such as…
Multiple-Attribute Text Style Transfer
Sandeep Subramanian, Guillaume Lample, Eric Michael Smith +3
The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "styl…
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal +4
Deep networks have achieved impressive results across a variety of important tasks. However a known weakness is a failure to perform well when evaluated on data which differ from t…
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
Sandeep Subramanian, Adam Trischler, Yoshua Bengio +1
A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised…
A Deep Reinforcement Learning Chatbot (Short Version)
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…
A Deep Reinforcement Learning Chatbot
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…
Neural Models for Key Phrase Detection and Question Generation
Sandeep Subramanian, Tong Wang, Xingdi Yuan +3
We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human…
Adversarial Generation of Natural Language
Sai Rajeswar, Sandeep Subramanian, Francis Dutil +2
Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the advers…
Machine Comprehension by Text-to-Text Neural Question Generation
Xingdi Yuan, Tong Wang, Caglar Gulcehre +5
We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervis…
Deep Complex Networks
Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang +7
At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and representations. However, recent work on r…
Neural Architectures for Named Entity Recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian +2
State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised train…