Papers by Rajiv Gupta
Enhancing multi-modal Relation Extraction with Reinforcement Learning Guided Graph Diffusion Framework (2025.coling-main)
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| Challenge: | Existing methods for cross-modal relation extraction focus on single-modal data, which limits their use in real-world situations. |
| Approach: | They propose a framework that leverages pre-trained models to encode multi-modal data into scene graphs and combine them into a cross-modal graph. |
| Outcome: | The proposed model outperforms existing methods on multi-modal relation extraction tasks. |
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks (2022.coling-1)
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| Challenge: | Quantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. |
| Approach: | They compare accuracy vs. model size tradeoffs using quantization and distillation methods . they find that pruning provides greater benefit than quantization . |
| Outcome: | The proposed methods reduce model size and can accelerate inference, but their relative benefit and combinatorial interactions have not been rigorously studied. |