activity
20162026
most citedIndicBART: A Pre-trained Model for Indic Natural Language Generation

77 citations · 258 across the 71 of their papers we have counts for

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Showing 2020Show all

10 papers · 1 filter

eess.IV2020

Unsupervised Deep Video Denoising

Dev Yashpal Sheth, Sreyas Mohan, Joshua L. Vincent +5

Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such…

cs.CL2020

Towards Interpreting BERT for Reading Comprehension Based QA

Sahana Ramnath, Preksha Nema, Deep Sahni +1

BERT and its variants have achieved state-of-the-art performance in various NLP tasks. Since then, various works have been proposed to analyze the linguistic information being capt…

cs.LG2020★ 1 cited

Evaluating a Generative Adversarial Framework for Information Retrieval

Ameet Deshpande, Mitesh M. Khapra

Recent advances in Generative Adversarial Networks (GANs) have resulted in its widespread applications to multiple domains. A recent model, IRGAN, applies this framework to Informa…

cs.CL2020

Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining

Ananya B. Sai, Akash Kumar Mohankumar, Siddhartha Arora +1

There is an increasing focus on model-based dialog evaluation metrics such as ADEM, RUBER, and the more recent BERT-based metrics. These models aim to assign a high score to all re…

cs.CL2020★ 2 cited

On the Importance of Local Information in Transformer Based Models

Madhura Pande, Aakriti Budhraja, Preksha Nema +2

The self-attention module is a key component of Transformer-based models, wherein each token pays attention to every other token. Recent studies have shown that these heads exhibit…

cs.CL2020

A Survey of Evaluation Metrics Used for NLG Systems

Ananya B. Sai, Akash Kumar Mohankumar, Mitesh M. Khapra

The success of Deep Learning has created a surge in interest in a wide a range of Natural Language Generation (NLG) tasks. Deep Learning has not only pushed the state of the art in…