Search Results for author: Sandeep Subramanian

Found 20 papers, 8 papers with code

NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022

no code implementations IWSLT (ACL) 2022 Oleksii Hrinchuk, Vahid Noroozi, Ashwinkumar Ganesan, Sarah Campbell, Sandeep Subramanian, Somshubra Majumdar, Oleksii Kuchaiev

Our cascade system consists of 1) Conformer RNN-T automatic speech recognition model, 2) punctuation-capitalization model based on pre-trained T5 encoder, 3) ensemble of Transformer neural machine translation models fine-tuned on TED talks.

Automatic Speech Recognition Machine Translation +1

NVIDIA NeMo Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21

no code implementations16 Nov 2021 Sandeep Subramanian, Oleksii Hrinchuk, Virginia Adams, Oleksii Kuchaiev

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.

Data Augmentation Knowledge Distillation +3

Multi-scale Transformer Language Models

no code implementations1 May 2020 Sandeep Subramanian, Ronan Collobert, Marc'Aurelio Ranzato, Y-Lan Boureau

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 to handle the hierarchical nature of language.

Language Modelling

Multiple-Attribute Text Rewriting

no code implementations ICLR 2019 Guillaume Lample, Sandeep Subramanian, Eric Smith, Ludovic Denoyer, Marc'Aurelio Ranzato, Y-Lan Boureau

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 "style".

Disentanglement Style Transfer +1

Towards Text Generation with Adversarially Learned Neural Outlines

no code implementations NeurIPS 2018 Sandeep Subramanian, Sai Rajeswar Mudumba, Alessandro Sordoni, Adam Trischler, Aaron C. Courville, Chris Pal

We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders.

Text Generation

Multiple-Attribute Text Style Transfer

3 code implementations1 Nov 2018 Sandeep Subramanian, Guillaume Lample, Eric Michael Smith, Ludovic Denoyer, Marc'Aurelio Ranzato, Y-Lan Boureau

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 "style".

Disentanglement Style Transfer +2

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

4 code implementations ICLR 2018 Sandeep Subramanian, Adam Trischler, Yoshua Bengio, Christopher J. Pal

In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model.

Multi-Task Learning Natural Language Inference +1

Adversarial Generation of Natural Language

no code implementations WS 2017 Sai Rajeswar, Sandeep Subramanian, Francis Dutil, Christopher Pal, Aaron Courville

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation.

Image Generation Language Modelling

Deep Complex Networks

8 code implementations ICLR 2018 Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher J. Pal

Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models.

Image Classification Music Transcription

Neural Architectures for Named Entity Recognition

41 code implementations NAACL 2016 Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer

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 training corpora that are available.

Named Entity Recognition NER

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