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
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…