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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…
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…
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…
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…
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…
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…
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…
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…