activity
20172022
most citedAutomatic Discovery of Novel Intents & Domains from Text Utterances

13 citations · 16 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CL20221 cited

Design Considerations For Hypothesis Rejection Modules In Spoken Language Understanding Systems

Aman Alok, Rahul Gupta, Shankar Ananthakrishnan

Spoken Language Understanding (SLU) systems typically consist of a set of machine learning models that operate in conjunction to produce an SLU hypothesis. The generated hypothesis…

cs.CL2021

ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification

Manoj Kumar, Varun Kumar, Hadrien Glaude +3

Practical sequence classification tasks in natural language processing often suffer from low training data availability for target classes. Recent works towards mitigating this pro…

cs.CL2020

Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks

Ansel MacLaughlin, Jwala Dhamala, Anoop Kumar +3

Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-…

cs.CL202013 cited

Automatic Discovery of Novel Intents & Domains from Text Utterances

Nikhita Vedula, Rahul Gupta, Aman Alok +1

One of the primary tasks in Natural Language Understanding (NLU) is to recognize the intents as well as domains of users' spoken and written language utterances. Most existing rese…

cs.CL2019

Fast Intent Classification for Spoken Language Understanding

Akshit Tyagi, Varun Sharma, Rahul Gupta +4

Spoken Language Understanding (SLU) systems consist of several machine learning components operating together (e.g. intent classification, named entity recognition and resolution).…

cs.CL2018

On Enhancing Speech Emotion Recognition using Generative Adversarial Networks

Saurabh Sahu, Rahul Gupta, Carol Espy-Wilson

Generative Adversarial Networks (GANs) have gained a lot of attention from machine learning community due to their ability to learn and mimic an input data distribution. GANs consi…