24 citations · 25 across the 4 of their papers we have counts for
6 papers
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…