Papers by Chandana Satya Prakash
MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources (2024.findings-acl)
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| Challenge: | Existing retrieval-augmented models typically retrieve information from a single type of knowledge source. |
| Approach: | They propose an efficient memory-augmented transformer to retrieve relevant knowledge from multiple knowledge sources. |
| Outcome: | The proposed model outperforms existing retrieval-augmented models on popular QA benchmarks in terms of accuracy and speed. |
Attention Fusion: a light yet efficient late fusion mechanism for task adaptation in NLU (2022.findings-naacl)
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| Challenge: | a recent study has shown that fine-tuning pre-trained models is parameter-inefficient and expensive. |
| Approach: | They propose a task-attuned token module which integrates pre-trained network representations into a pre-trainer. |
| Outcome: | The proposed model trains only 0.0009% of the parameters and is efficient during computation and scalable during deployment. |
Instilling Type Knowledge in Language Models via Multi-Task QA (2022.findings-naacl)
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| Challenge: | Current methods to learn entity types rely on coarse, noisy labels . current methods rely only on text-to-text pre-training on type-centric questions . |
| Approach: | They propose to instill fine-grained type knowledge in language models by pre-training on type-centric questions. |
| Outcome: | The proposed model achieves state-of-the-art in zero-shot dialog state tracking benchmarks and can accurately infer entity types in Wikipedia articles. |