activity
20202026
most citedA Benchmark and Dataset for Post-OCR text correction in Sanskrit

2 citations · 5 across the 11 of their papers we have counts for

collaborators

12 papers

cs.CL2026

Pingala: Prosody-Aware Decoding for Sanskrit Poetry Generation

Manoj Balaji Jagadeeshan, Atul Singh, Nallani Chakravartula Sahith +2

Poetry generation in Sanskrit typically requires the verse to be semantically coherent and adhere to strict prosodic rules. In Sanskrit prosody, every line of a verse is typically…

cs.CL2025

Mahānāma: A Unique Testbed for Literary Entity Discovery and Linking

Sujoy Sarkar, Gourav Sarkar, Manoj Balaji Jagadeeshan +3

High lexical variation, ambiguous references, and long-range dependencies make entity resolution in literary texts particularly challenging. We present Mahānāma, the first large-sc…

cs.CL2024

CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages

Pretam Ray, Jivnesh Sandhan, Amrith Krishna +1

Neural dependency parsing has achieved remarkable performance for low resource morphologically rich languages. It has also been well-studied that morphologically rich languages exh…

cs.CL2024★ 1 cited

A Three-Pronged Approach to Cross-Lingual Adaptation with Multilingual LLMs

Vaibhav Singh, Amrith Krishna, Karthika NJ +1

Low-resource languages, by its very definition, tend to be under represented in the pre-training corpora of Large Language Models. In this work, we investigate three low-resource c…

cs.CL2023

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…

cs.CL2023

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…