2020
EMNLP
EMNLP 2020
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation
Abstract
AbstractNegation is underrepresented in existing natural language inference benchmarks. Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments. In this paper, we present a new benchmark for natural language inference in which negation plays a critical role. We also show that state-of-the-art transformers struggle making inference judgments with the new pairs.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— evaluation benchmark
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio