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20182022
most citedTemporal Common Sense Acquisition with Minimal Supervision

12 citations · 24 across the 8 of their papers we have counts for

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15 papers · 1 filter

cs.CL2022

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…

cs.CL20211 cited

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…

cs.CL2021

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…

cs.CL202012 cited

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…

cs.CL20204 cited

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…

cs.CL2020

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…