12 citations · 24 across the 8 of their papers we have counts for
20 papers
Answer Consolidation: Formulation and Benchmarking
Wenxuan Zhou, Qiang Ning, Heba Elfardy +2
Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-wo…
SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning
Roshanak Mirzaee, Hossein Rajaby Faghihi, Qiang Ning +1
This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and…
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning
Rujun Han, I-Hung Hsu, Jiao Sun +4
Understanding how events are semantically related to each other is the essence of reading comprehension. Recent event-centric reading comprehension datasets focus mostly on event a…
Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq
Qiang Ning, Hao Wu, Pradeep Dasigi +5
High-quality and large-scale data are key to success for AI systems. However, large-scale data annotation efforts are often confronted with a set of common challenges: (1) designin…
Learnability with Indirect Supervision Signals
Kaifu Wang, Qiang Ning, Dan Roth
Learning from indirect supervision signals is important in real-world AI applications when, often, gold labels are missing or too costly. In this paper, we develop a unified theore…
Temporal Common Sense Acquisition with Minimal Supervision
Ben Zhou, Qiang Ning, Daniel Khashabi +1
Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such informati…