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Qiang Ning
Researcher at Amazon.com
Publications - 73
Citations - 1888
Qiang Ning is an academic researcher from Amazon.com. The author has contributed to research in topics: Relationship extraction & Natural language. The author has an hindex of 20, co-authored 55 publications receiving 1132 citations. Previous affiliations of Qiang Ning include Allen Institute for Artificial Intelligence & Tsinghua University.
Papers
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Proceedings ArticleDOI
Evaluating Models’ Local Decision Boundaries via Contrast Sets
Matt Gardner,Yoav Artzi,Victoria Basmov,Jonathan Berant,Ben Bogin,Sihao Chen,Pradeep Dasigi,Dheeru Dua,Yanai Elazar,Ananth Gottumukkala,Nitish Gupta,Hannaneh Hajishirzi,Gabriel Ilharco,Daniel Khashabi,Kevin Lin,Jiangming Liu,Nelson F. Liu,Phoebe Mulcaire,Qiang Ning,Sameer Singh,Noah A. Smith,Sanjay Subramanian,Reut Tsarfaty,Eric Wallace,Ally Zhang,Ben Zhou +25 more
TL;DR: A more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data, and recommends that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets.
Proceedings ArticleDOI
Joint Reasoning for Temporal and Causal Relations
TL;DR: This paper forms the joint problem as an integer linear programming (ILP) problem, enforcing constraints that are inherent in the nature of time and causality, and shows that the joint inference framework results in statistically significant improvement in the extraction of both temporal and causal relations from text.
Posted Content
"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
TL;DR: It is found that the best current methods used on MCTACO are still far behind human performance, by about 20%, and several directions for improvement are discussed.
Proceedings ArticleDOI
A Multi-Axis Annotation Scheme for Event Temporal Relations
Qiang Ning,Hao Wu,Dan Roth +2 more
TL;DR: This article proposed a new multi-axis modeling to better capture the temporal structure of events and identified that event end-points are a major source of confusion in annotation, so they also propose to annotate TempRels based on start-points only.
Proceedings ArticleDOI
A Structured Learning Approach to Temporal Relation Extraction
Qiang Ning,Zhili Feng,Dan Roth +2 more
TL;DR: This article proposed a structured learning approach to identify temporal relations between events in a story, which takes these dependencies into account while learning to identify these relations and proposes a new perspective on handling missing relations, a known issue that hurts existing methods.