activity
20232026
most citedSarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection

1 citations · 2 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CL2026

IndicQE-APE: A Consolidated Benchmark for Quality Estimation and Automatic Post-Editing for Indic Languages

Diptesh Kanojia, Archchana Sindhujan, Sourabh Deoghare +14

Indic quality estimation (QE) and automatic post-editing (APE) data is spread across separate releases, so no single resource supports training and evaluation across tasks and lang…

cs.CL2026

Assessing and Improving Punctuation Robustness in English-Marathi Machine Translation

Kaustubh Shivshankar Shejole, Sourabh Deoghare, Pushpak Bhattacharyya

Neural Machine Translation (NMT) systems rely heavily on explicit punctuation cues to resolve semantic ambiguities in a source sentence. Inputting user-generated sentences, which a…

cs.CL2025

Improving Text Style Transfer using Masked Diffusion Language Models with Inference-time Scaling

Tejomay Kishor Padole, Suyash P Awate, Pushpak Bhattacharyya

Masked diffusion language models (MDMs) have recently gained traction as a viable generative framework for natural language. This can be attributed to its scalability and ease of t…

cs.CL2025

Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained Decoding for Automatic Post-Editing

Sourabh Deoghare, Diptesh Kanojia, Pushpak Bhattacharyya

Automatic Post-Editing (APE) systems often struggle with over-correction, where unnecessary modifications are made to a translation, diverging from the principle of minimal editing…

cs.CL2024

Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages

Sourabh Deoghare, Diptesh Kanojia, Pushpak Bhattacharyya

This exploratory study investigates the potential of multilingual Automatic Post-Editing (APE) systems to enhance the quality of machine translations for low-resource Indo-Aryan la…

cs.LG2024★ 1 cited

Material Microstructure Design Using VAE-Regression with Multimodal Prior

Avadhut Sardeshmukh, Sreedhar Reddy, BP Gautham +1

We propose a variational autoencoder (VAE)-based model for building forward and inverse structure-property linkages, a problem of paramount importance in computational materials sc…

cs.CL2023

APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation

Akshay Batheja, Sourabh Deoghare, Diptesh Kanojia +1

Automatic Post-Editing (APE) is the task of automatically identifying and correcting errors in the Machine Translation (MT) outputs. We propose a repair-filter-use methodology that…

cs.CL2023

Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning

Sapan Shah, Sreedhar Reddy, Pushpak Bhattacharyya

We present a novel retrofitting method to induce emotion aspects into pre-trained language models (PLMs) such as BERT and RoBERTa. Our method updates pre-trained network weights us…

cs.CL2023★ 1 cited

Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection

Swapnil Bhosale, Abhra Chaudhuri, Alex Lee Robert Williams +5

The introduction of the MUStARD dataset, and its emotion recognition extension MUStARD++, have identified sarcasm to be a multi-modal phenomenon -- expressed not only in natural la…