Papers by Prakhar Sharma
IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension (D19-60)
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| Challenge: | Using pre-trained language models, we can model machine comprehension using commonsense reasoning. |
| Approach: | They propose a machine comprehension model that leverages pre-trained language models over commonsense knowledge bases. |
| Outcome: | The proposed model improves on baseline models and other commonsense knowledge bases. |
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment (2022.findings-acl)
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| Challenge: | Existing work on question answering focuses on the pre-deployment stage; building an accurate model for deployment. |
| Approach: | They collect feedback from users and train a neural model with the feedback data. |
| Outcome: | The proposed model can explain the correctness or incorrectness of an answer. |