Papers by Prayushi Faldu
RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questions (2024.acl-long)
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| Challenge: | Existing knowledge base question answering models assume all questions to be answerable. |
| Approach: | They propose a new KBQA model that unifies two key ideas in a single architecture . they propose logical form discrimination and sketch-filling-based construction for unanswerable questions . |
| Outcome: | The proposed model outperforms existing models in handling answerable and unanswerable questions. |
Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions (2023.acl-long)
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| Challenge: | missing facts, incomplete schema and limited scope lead to many questions being unanswerable. |
| Approach: | They propose to adapt a KBQA dataset with unanswerable questions to detect missing facts and incomplete schema. |
| Outcome: | The proposed model performs poorly even after adaptation for unanswerable questions. |