Armin Oliya — accepted AI-conference papers

2021

End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs

Amir Saffari, Armin Oliya, Priyanka Sen, Tom Ayoola

EMNLP 2021main

Recently, end-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) have delivered promising results using only a weakly supervised dataset. However, these models are trained and evaluated in a setting where hand-annotated question entities are supplied to the model, leavin…

Cited by 24 Source ↗PDF ↗Scholar ↗

2021

Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection

Priyanka Sen, Armin Oliya, Amir Saffari

EMNLP 2021main

End-to-end question answering using a differentiable knowledge graph is a promising technique that requires only weak supervision, produces interpretable results, and is fully differentiable. Previous implementations of this technique (Cohen et al, 2020) have focused on single-entity questions using…

Cited by 21 Source ↗PDF ↗Scholar ↗