b'Qiang Ning'

Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer

Extracting temporal relations (e.g., before, after, concurrent) among ev...
04/19/2021 ∙ by Shuaicheng Zhang, et al. ∙ 0 ∙ share

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ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning

Stories and narratives are composed based on a variety of events. Unders...
04/16/2021 ∙ by Rujun Han, et al. ∙ 9 ∙ share

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SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning

This paper proposes a question-answering (QA) benchmark for spatial reas...
04/12/2021 ∙ by Roshanak Mirzaee, et al. ∙ 0 ∙ share

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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

Matt Gardner

Ashish Sabharwal

Tushar Khot

Daniel Khashabi

Kevin Lin

Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer

ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning

SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning

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