71 citations · 184 across the 23 of their papers we have counts for
24 papers
PInKS: Preconditioned Commonsense Inference with Minimal Supervision
Ehsan Qasemi, Piyush Khanna, Qiang Ning +1
Reasoning with preconditions such as "glass can be used for drinking water unless the glass is shattered" remains an open problem for language models. The main challenge lies in th…
Answer Consolidation: Formulation and Benchmarking
Wenxuan Zhou, Qiang Ning, Heba Elfardy +2
Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-wo…
SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning
Roshanak Mirzaee, Hossein Rajaby Faghihi, Qiang Ning +1
This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and…
Extracting Temporal Event Relation with Syntax-guided Graph Transformer
Shuaicheng Zhang, Lifu Huang, Qiang Ning
Extracting temporal relations (e.g., before, after, and simultaneous) among events is crucial to natural language understanding. One of the key challenges of this problem is that w…
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning
Rujun Han, I-Hung Hsu, Jiao Sun +4
Understanding how events are semantically related to each other is the essence of reading comprehension. Recent event-centric reading comprehension datasets focus mostly on event a…
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…
Temporal Reasoning on Implicit Events from Distant Supervision
Ben Zhou, Kyle Richardson, Qiang Ning +3
We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events -- events that are not mentioned explicitly in natural l…
Learnability with Indirect Supervision Signals
Kaifu Wang, Qiang Ning, Dan Roth
Learning from indirect supervision signals is important in real-world AI applications when, often, gold labels are missing or too costly. In this paper, we develop a unified theore…
Foreseeing the Benefits of Incidental Supervision
Hangfeng He, Mingyuan Zhang, Qiang Ning +1
Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals. These could include partial labels, noisy labels, knowledge-bas…
Temporal Common Sense Acquisition with Minimal Supervision
Ben Zhou, Qiang Ning, Daniel Khashabi +1
Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such informati…
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
Qiang Ning, Hao Wu, Rujun Han +3
A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly st…
Evaluating Models' Local Decision Boundaries via Contrast Sets
Matt Gardner, Yoav Artzi, Victoria Basmova +23
Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluation…
"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
Ben Zhou, Daniel Khashabi, Qiang Ning +1
Understanding time is crucial for understanding events expressed in natural language. Because people rarely say the obvious, it is often necessary to have commonsense knowledge abo…
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction
Rujun Han, Qiang Ning, Nanyun Peng
We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing…
An Improved Neural Baseline for Temporal Relation Extraction
Qiang Ning, Sanjay Subramanian, Dan Roth
Determining temporal relations (e.g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amo…
QuASE: Question-Answer Driven Sentence Encoding
Hangfeng He, Qiang Ning, Dan Roth
Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typical…
A Structured Learning Approach to Temporal Relation Extraction
Qiang Ning, Zhili Feng, Dan Roth
Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on,…
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…
CogCompTime: A Tool for Understanding Time in Natural Language Text
Qiang Ning, Ben Zhou, Zhili Feng +2
Automatic extraction of temporal information in text is an important component of natural language understanding. It involves two basic tasks: (1) Understanding time expressions th…
Partial Or Complete, That's The Question
Qiang Ning, Hangfeng He, Chuchu Fan +1
For many structured learning tasks, the data annotation process is complex and costly. Existing annotation schemes usually aim at acquiring completely annotated structures, under t…
An information theoretic model for summarization, and some basic results
Eric Graves, Qiang Ning, Prithwish Basu
A basic information theoretic model for summarization is formulated. Here summarization is considered as the process of taking a report of binary objects, and producing from it…
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…
Exploiting Partially Annotated Data for Temporal Relation Extraction
Qiang Ning, Zhongzhi Yu, Chuchu Fan +1
Annotating temporal relations (TempRel) between events described in natural language is known to be labor intensive, partly because the total number of TempRels is quadratic in the…
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…