6 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.CL2021
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset
Qiyuan Zhang, Lei Wang, Sicheng Yu +4
While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall…
cs.CL2020★ 6 cited
Counterfactual Variable Control for Robust and Interpretable Question Answering
Sicheng Yu, Yulei Niu, Shuohang Wang +2
Deep neural network based question answering (QA) models are neither robust nor explainable in many cases. For example, a multiple-choice QA model, tested without any input of ques…
cs.CL2020
Context Modeling with Evidence Filter for Multiple Choice Question Answering
Sicheng Yu, Hao Zhang, Wei Jing +1
Multiple-Choice Question Answering (MCQA) is a challenging task in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that su…