2 citations · 2 across the 1 of their papers we have counts for
4 papers
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…
Joint Reasoning for Temporal and Causal Relations
Qiang Ning, Zhili Feng, Hao Wu +1
Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must be before its effect in time, temporal and cau…
A Multi-Axis Annotation Scheme for Event Temporal Relations
Qiang Ning, Hao Wu, Dan Roth
Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a be…
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
Qiang Ning, Hao Wu, Haoruo Peng +1
Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the…