Papers by Armin Oliya
End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs (2021.emnlp-main)
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| Challenge: | End-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) are effective, but training a weakly supervised dataset is difficult. |
| Approach: | They extend the boundaries of E2E learning for KGQA to include the training of an ER component. |
| Outcome: | The proposed model is fully differentiable thanks to a recent method for building differentiably KGs. |
Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection (2021.emnlp-main)
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| Challenge: | Existing models that handle single-entity questions have focused on relation following . introducing intersection improves performance on multiple-entities questions by over 14% . |
| Approach: | They propose a model that explicitly handles multiple-entity questions by implementing an intersection operation. |
| Outcome: | The proposed model improves on multiple-entity questions by over 14% on two datasets . it also improves performance on questions with multiple entities by 19% . |