24 citations · 25 across the 8 of their papers we have counts for
10 papers
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery
Bhavuk Singhal, Ashim Gupta, Shivasankaran V P +1
Identifying intents from dialogue utterances forms an integral component of task-oriented dialogue systems. Intent-related tasks are typically formulated either as a classification…
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems
Ashim Gupta, Amrith Krishna
Clean-label (CL) attack is a form of data poisoning attack where an adversary modifies only the textual input of the training data, without requiring access to the labeling functio…
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text
Ashim Gupta, Carter Wood Blum, Temma Choji +4
Can language models transform inputs to protect text classifiers against adversarial attacks? In this work, we present ATINTER, a model that intercepts and learns to rewrite advers…
Sāmayik: A Benchmark and Dataset for English-Sanskrit Translation
Ayush Maheshwari, Ashim Gupta, Amrith Krishna +4
We release Sāmayik, a dataset of around 53,000 parallel English-Sanskrit sentences, written in contemporary prose. Sanskrit is a classical language still in sustenance and has a ri…
Evaluating Relaxations of Logic for Neural Networks: A Comprehensive Study
Mattia Medina Grespan, Ashim Gupta, Vivek Srikumar
Symbolic knowledge can provide crucial inductive bias for training neural models, especially in low data regimes. A successful strategy for incorporating such knowledge involves re…
X-FACT: A New Benchmark Dataset for Multilingual Fact Checking
Ashim Gupta, Vivek Srikumar
In this work, we introduce X-FACT: the largest publicly available multilingual dataset for factual verification of naturally existing real-world claims. The dataset contains short…
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages
Jivnesh Sandhan, Amrith Krishna, Ashim Gupta +2
Neural dependency parsing has achieved remarkable performance for many domains and languages. The bottleneck of massive labeled data limits the effectiveness of these approaches fo…
BERT & Family Eat Word Salad: Experiments with Text Understanding
Ashim Gupta, Giorgi Kvernadze, Vivek Srikumar
In this paper, we study the response of large models from the BERT family to incoherent inputs that should confuse any model that claims to understand natural language. We define s…
Evaluating Neural Morphological Taggers for Sanskrit
Ashim Gupta, Amrith Krishna, Pawan Goyal +1
Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskri…
Neural Approaches for Data Driven Dependency Parsing in Sanskrit
Amrith Krishna, Ashim Gupta, Deepak Garasangi +3
Data-driven approaches for dependency parsing have been of great interest in Natural Language Processing for the past couple of decades. However, Sanskrit still lacks a robust pure…