13 citations · 27 across the 9 of their papers we have counts for
15 papers
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
Abdelrahman Zayed, Prasanna Parthasarathi, Goncalo Mordido +3
Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity concerns. Prediction models may discover, us…
Local Structure Matters Most in Most Languages
Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq +1
Many recent perturbation studies have found unintuitive results on what does and does not matter when performing Natural Language Understanding (NLU) tasks in English. Coding prope…
Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes
Louis Clouâtre, Prasanna Parthasarathi, Amal Zouaq +1
Providing better language tools for low-resource and endangered languages is imperative for equitable growth. Recent progress with massively multilingual pretrained models has prov…
Memory Augmented Optimizers for Deep Learning
Paul-Aymeric McRae, Prasanna Parthasarathi, Mahmoud Assran +1
Popular approaches for minimizing loss in data-driven learning often involve an abstraction or an explicit retention of the history of gradients for efficient parameter updates. Th…
Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?
Prasanna Parthasarathi, Joelle Pineau, Sarath Chandar
Predicting the next utterance in dialogue is contingent on encoding of users' input text to generate appropriate and relevant response in data-driven approaches. Although the seman…
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss
Prasanna Parthasarathi, Mohamed Abdelsalam, Joelle Pineau +1
Neural models trained for next utterance generation in dialogue task learn to mimic the n-gram sequences in the training set with training objectives like negative log-likelihood (…