b'Qiang Ning'

research

∙ 06/16/2022

PInKS: Preconditioned Commonsense Inference with Minimal Supervision

Reasoning with preconditions such as "glass can be used for drinking wat...

Ehsan Qasemi, et al. ∙

research

∙ 04/29/2022

Answer Consolidation: Formulation and Benchmarking

Current question answering (QA) systems primarily consider the single-an...

Wenxuan Zhou, et al. ∙

research

∙ 04/19/2021

Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer

Extracting temporal relations (e.g., before, after, concurrent) among ev...

Shuaicheng Zhang, et al. ∙

research

∙ 04/16/2021

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

Stories and narratives are composed based on a variety of events. Unders...

Rujun Han, et al. ∙

research

∙ 04/12/2021

SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning

This paper proposes a question-answering (QA) benchmark for spatial reas...

Roshanak Mirzaee, et al. ∙

research

∙ 10/24/2020

Temporal Reasoning on Implicit Events from Distant Supervision

Existing works on temporal reasoning among events described in text focu...

Ben Zhou, et al. ∙

research

∙ 10/06/2020

Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq

High-quality and large-scale data are key to success for AI systems. How...

Qiang Ning, et al. ∙

research

∙ 06/15/2020

Learnability with Indirect Supervision Signals

Learning from indirect supervision signals is important in real-world AI...

Kaifu Wang, et al. ∙

research

∙ 06/09/2020

Foreshadowing the Benefits of Incidental Supervision

Learning theory mostly addresses the standard learning paradigm, assumin...

Hangfeng He, et al. ∙

research

∙ 05/08/2020

Temporal Common Sense Acquisition with Minimal Supervision

Temporal common sense (e.g., duration and frequency of events) is crucia...

Ben Zhou, et al. ∙

research

∙ 05/01/2020

TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

A critical part of reading is being able to understand the temporal rela...

Qiang Ning, et al. ∙

research

∙ 04/06/2020

Evaluating NLP Models via Contrast Sets

Standard test sets for supervised learning evaluate in-distribution gene...

Matt Gardner, et al. ∙

research

∙ 09/06/2019

"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...

Ben Zhou, et al. ∙

research

∙ 09/02/2019

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

We propose a joint event and temporal relation extraction model with sha...

Rujun Han, et al. ∙

research

∙ 09/01/2019

An Improved Neural Baseline for Temporal Relation Extraction

Determining temporal relations (e.g., before or after) between events ha...

Qiang Ning, et al. ∙

research

∙ 09/01/2019

Incidental Supervision from Question-Answering Signals

Human annotations are costly for many natural language processing (NLP) ...

Hangfeng He, et al. ∙

research

∙ 06/12/2019

A Structured Learning Approach to Temporal Relation Extraction

Identifying temporal relations between events is an essential step towar...

Qiang Ning, et al. ∙

research

∙ 06/12/2019

Joint Reasoning for Temporal and Causal Relations

Understanding temporal and causal relations between events is a fundamen...

Qiang Ning, et al. ∙

research

∙ 06/12/2019

CogCompTime: A Tool for Understanding Time in Natural Language Text

Automatic extraction of temporal information in text is an important com...

Qiang Ning, et al. ∙

research

∙ 06/12/2019

Partial Or Complete, That's The Question

For many structured learning tasks, the data annotation process is compl...

Qiang Ning, et al. ∙

research

∙ 01/18/2019

An information theoretic model for summarization, and some basic results

A basic information theoretic model for summarization is formulated. Her...

Eric Graves, et al. ∙

research

∙ 04/20/2018

A Multi-Axis Annotation Scheme for Event Temporal Relations

Existing temporal relation (TempRel) annotation schemes often have low i...

Qiang Ning, et al. ∙

research

∙ 04/18/2018

Exploiting Partially Annotated Data for Temporal Relation Extraction

Annotating temporal relations (TempRel) between events described in natu...

Qiang Ning, et al. ∙

research

∙ 04/17/2018

Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource

Extracting temporal relations (before, after, overlapping, etc.) is a ke...

Qiang Ning, et al. ∙

Qiang Ning

Featured Co-authors

Hao Wu

Dan Roth

Noah A. Smith

Nanyun Peng

Jonathan Berant

Sameer Singh

Muhao Chen

Matt Gardner

Ashish Sabharwal

Kevin Lin

Yoav Artzi

PInKS: Preconditioned Commonsense Inference with Minimal Supervision

Answer Consolidation: Formulation and Benchmarking

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