13 citations · 43 across the 26 of their papers we have counts for
6 papers · 1 filter
Local Structure Matters Most: Perturbation Study in NLU
Louis Clouatre, Prasanna Parthasarathi, Amal Zouaq +1
Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models are surprisingly insensitive to the orde…
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 (…
Sometimes We Want Translationese
Prasanna Parthasarathi, Koustuv Sinha, Joelle Pineau +1
Rapid progress in Neural Machine Translation (NMT) systems over the last few years has been driven primarily towards improving translation quality, and as a secondary focus, improv…
UnNatural Language Inference
Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau +1
Recent investigations into the inner-workings of state-of-the-art large-scale pre-trained Transformer-based Natural Language Understanding (NLU) models indicate that they appear to…