Papers by Manish Kumar
De-Mixing Sentiment from Code-Mixed Text (P19-2)
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| Challenge: | Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. |
| Approach: | They propose a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data using CNNs to generate subword representations for the sentences. |
| Outcome: | The proposed architecture achieves 83.54% accuracy and 0.827 F1 score on a benchmark dataset. |
SCULPT: Systematic Tuning of Long Prompts (2025.acl-long)
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Shanu Kumar, Akhila Yesantarao Venkata, Shubhanshu Khandelwal, Bishal Santra, Parag Agrawal, Manish Gupta
| Challenge: | Existing methods for prompt optimization struggle with longer, more complex ones, often risking information loss and being sensitive to small perturbations. |
| Approach: | They propose a framework that treats prompt optimization as a hierarchical tree refinement problem and uses a Critic-Actor framework to generate reflections and apply actions to refine the prompt. |
| Outcome: | The proposed framework produces more stable and interpretable prompt modifications, ensuring better generalization across tasks. |
Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation (2020.aacl-main)
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| Challenge: | Current neural network-based questions generation techniques take only one or two sentences as input. |
| Approach: | They propose a simple yet effective technique for question generation from paragraphs . they augment a sequence-to-sequence QG model with dynamic, paragraph-specific dictionary . |
| Outcome: | The proposed model outperforms state-of-the-art systems in question generation from paragraphs in automatic and human evaluation. |
STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module (2024.acl-short)
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| Challenge: | Topic modeling is a widely used technique to analyze large document corpora. |
| Approach: | They propose a module for topic retrieval, exploration, and analysis that implements multiple intruder-word based topic evaluation metrics. |
| Outcome: | The proposed module implements multiple intruder-word based topic evaluation metrics and extends existing datasets. |