40 citations · 56 across the 4 of their papers we have counts for
4 papers
Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
Priyanka Sen, Alham Fikri Aji, Amir Saffari
We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answ…
End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs
Armin Oliya, Amir Saffari, Priyanka Sen +1
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, thes…
Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection
Priyanka Sen, Amir Saffari, Armin Oliya
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 diff…
What do Models Learn from Question Answering Datasets?
Priyanka Sen, Amir Saffari
While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering its…