Papers by Subhadarshi Panda
Shuffled-token Detection for Refining Pre-trained RoBERTa (2021.naacl-srw)
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| Challenge: | State-of-the-art transformer models have achieved robust performance on a variety of NLP tasks. |
| Approach: | They propose to refine a pre-trained NLP model by detecting shuffled tokens . they use a sequential approach to train a model using random shuffling . |
| Outcome: | The proposed model achieves better performance on 4 out of 7 GLUE tasks. |
Using Neural Machine Translation for Generating Diverse Challenging Exercises for Language Learner (2023.acl-long)
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| Challenge: | a common challenge for language learners is understanding how to appropriately use words that may have similar meanings but are used in different contexts. |
| Approach: | They propose a method to automatically generate distractors for cloze exercises for English language learners using round-trip neural machine translation. |
| Outcome: | The proposed method generates distractors for cloze exercises for English learners . it shows that the generated distractors are of the same difficulty as human distractors . |
Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection (2022.acl-srw)
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| Challenge: | Deception detection is a deliberate choice to mislead to gain some advantage or avoid some penalty. |
| Approach: | They propose to use inter-domain distance to identify suitable source domain for a given target domain to improve cross-domain deception classification and to better understand multi-modal deception detection. |
| Outcome: | The proposed methods will be able to detect deception in cross-domain, cross-lingual and multi-modal settings and will improve multi-modular deception classification. |
RG-VQA: Leveraging Retriever-Generator Pipelines for Knowledge Intensive Visual Question Answering (2025.findings-emnlp)
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Settaluri Lakshmi Sravanthi, Pulkit Agarwal, Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda, Ankit Mishra, Kiran Pradeep, Srihari K B, Godawari Sudhakar Rao, Pushpak Bhattacharyya
| Challenge: | Existing methods to improve the reasoning capabilities of VQA systems are limited due to complexity of graph neural networks and end-to-end training. |
| Approach: | They propose a method to integrate Dense Passage Retrievers with Vision Language Models to boost the reasoning capabilities of VQA systems. |
| Outcome: | The proposed method outperforms human accuracy and GPT-4 in the ScienceQA dataset. |
Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation (2022.acl-srw)
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| Challenge: | a fill-in-the-blank exercise involves removing one word from a sentence and generating distractors . a valid distractor is a word that does not fit the context, and distractors are invalid . |
| Approach: | They propose to automatically generate distractors using round-trip neural machine translation . they show that using hundreds of translations for a given sentence generates a rich set of distractors . |
| Outcome: | The proposed method outperforms two strong baselines against a real corpus of cloze exercises and manually checks for validity. |
Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation (2022.lrec-1)
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Idris Abdulmumin, Satya Ranjan Dash, Musa Abdullahi Dawud, Shantipriya Parida, Shamsuddeen Muhammad, Ibrahim Sa’id Ahmad, Subhadarshi Panda, Ondřej Bojar, Bashir Shehu Galadanci, Bello Shehu Bello
| Challenge: | Hausa is considered a low resource language in natural language processing due to lack of resources. |
| Approach: | They propose a dataset that contains the description of an image in Hausa and its equivalent in English. |
| Outcome: | The Hausa Visual Genome is the first dataset of its kind . it can be used for Hausa-English machine translation, multi-modal research, image description . |
From Perception to Reasoning: Enhancing Vision-Language Models for Mobile UI Understanding (2025.findings-acl)
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Settaluri Lakshmi Sravanthi, Ankit Mishra, Debjyoti Mondal, Subhadarshi Panda, Rituraj Singh, Pushpak Bhattacharyya
| Challenge: | Accurately grounding visual and textual elements within mobile user interfaces remains a challenge for Vision-Language Models (VLMs). |
| Approach: | They propose a mobile UI understanding model trained on a dataset specifically tailored for mobile screen understanding and grounding. |
| Outcome: | The proposed model achieves significant gains in accuracy across all perception tasks and on reasoning benchmarks. |