Qiang Ning

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Amazon ∙ University of Illinois at Urbana-Champaign ∙ Allen Institute for Artificial Intelligence

Temporal Reasoning on Implicit Events from Distant Supervision

Existing works on temporal reasoning among events described in text focu...
10/24/2020 ∙ by Ben Zhou, et al. ∙ 0 ∙ share

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Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq

High-quality and large-scale data are key to success for AI systems. How...
10/06/2020 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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Learnability with Indirect Supervision Signals

Learning from indirect supervision signals is important in real-world AI...
06/15/2020 ∙ by Kaifu Wang, et al. ∙ 0 ∙ share

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Foreshadowing the Benefits of Incidental Supervision

Learning theory mostly addresses the standard learning paradigm, assumin...
06/09/2020 ∙ by Hangfeng He, et al. ∙ 19 ∙ share

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Temporal Common Sense Acquisition with Minimal Supervision

Temporal common sense (e.g., duration and frequency of events) is crucia...
05/08/2020 ∙ by Ben Zhou, et al. ∙ 0 ∙ share

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TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

A critical part of reading is being able to understand the temporal rela...
05/01/2020 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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Evaluating NLP Models via Contrast Sets

Standard test sets for supervised learning evaluate in-distribution gene...
04/06/2020 ∙ by Matt Gardner, et al. ∙ 0 ∙ share

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"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding

Understanding time is crucial for understanding events expressed in natu...
09/06/2019 ∙ by Ben Zhou, et al. ∙ 0 ∙ share

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Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

We propose a joint event and temporal relation extraction model with sha...
09/02/2019 ∙ by Rujun Han, et al. ∙ 0 ∙ share

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An Improved Neural Baseline for Temporal Relation Extraction

Determining temporal relations (e.g., before or after) between events ha...
09/01/2019 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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Incidental Supervision from Question-Answering Signals

Human annotations are costly for many natural language processing (NLP) ...
09/01/2019 ∙ by Hangfeng He, et al. ∙ 12 ∙ share

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A Structured Learning Approach to Temporal Relation Extraction

Identifying temporal relations between events is an essential step towar...
06/12/2019 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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Joint Reasoning for Temporal and Causal Relations

Understanding temporal and causal relations between events is a fundamen...
06/12/2019 ∙ by Qiang Ning, et al. ∙ 3 ∙ share

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CogCompTime: A Tool for Understanding Time in Natural Language Text

Automatic extraction of temporal information in text is an important com...
06/12/2019 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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Partial Or Complete, That's The Question

For many structured learning tasks, the data annotation process is compl...
06/12/2019 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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An information theoretic model for summarization, and some basic results

A basic information theoretic model for summarization is formulated. Her...
01/18/2019 ∙ by Eric Graves, et al. ∙ 0 ∙ share

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A Multi-Axis Annotation Scheme for Event Temporal Relations

Existing temporal relation (TempRel) annotation schemes often have low i...
04/20/2018 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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Exploiting Partially Annotated Data for Temporal Relation Extraction

Annotating temporal relations (TempRel) between events described in natu...
04/18/2018 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource

Extracting temporal relations (before, after, overlapping, etc.) is a ke...
04/17/2018 ∙ by Qiang Ning, et al. ∙ 0 ∙ share

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Qiang Ning

Featured Co-authors

Dan Roth

Noah A. Smith

Hao Wu

Jonathan Berant

Nanyun Peng

Sameer Singh

Ashish Sabharwal

Matt Gardner

Tushar Khot

Daniel Khashabi

Yoav Artzi

Temporal Reasoning on Implicit Events from Distant Supervision

Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq

Learnability with Indirect Supervision Signals

Foreshadowing the Benefits of Incidental Supervision

Temporal Common Sense Acquisition with Minimal Supervision

TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

Evaluating NLP Models via Contrast Sets

"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding

Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

An Improved Neural Baseline for Temporal Relation Extraction

Incidental Supervision from Question-Answering Signals

A Structured Learning Approach to Temporal Relation Extraction

Joint Reasoning for Temporal and Causal Relations

CogCompTime: A Tool for Understanding Time in Natural Language Text

Partial Or Complete, That's The Question

An information theoretic model for summarization, and some basic results

A Multi-Axis Annotation Scheme for Event Temporal Relations

Exploiting Partially Annotated Data for Temporal Relation Extraction

Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource