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A large annotated corpus for learning natural language inference

A large annotated corpus for learning natural language inference

Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015
21 August 2015
Samuel R. Bowman
Gabor Angeli
Christopher Potts
Christopher D. Manning
ArXiv (abs)PDFHTML

Papers citing "A large annotated corpus for learning natural language inference"

50 / 2,429 papers shown
Title
Does Self-Rationalization Improve Robustness to Spurious Correlations?
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Modeling Information Change in Science Communication with Semantically
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Retrieval Augmentation for Commonsense Reasoning: A Unified Approach
Retrieval Augmentation for Commonsense Reasoning: A Unified ApproachConference on Empirical Methods in Natural Language Processing (EMNLP), 2022
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Chenguang Zhu
Zhihan Zhang
Shuohang Wang
Zhuosheng Zhang
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Meng Jiang
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174
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Lexical Generalization Improves with Larger Models and Longer Training
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DiscoSense: Commonsense Reasoning with Discourse Connectives
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Vincent Ng
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PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models
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ADDMU: Detection of Far-Boundary Adversarial Examples with Data and
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ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty EstimationConference on Empirical Methods in Natural Language Processing (EMNLP), 2022
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Meta-learning Pathologies from Radiology Reports using Variance Aware
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157
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Enhancing Tabular Reasoning with Pattern Exploiting Training
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Abhilash Shankarampeta
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242
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Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of
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125
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Efficiently Tuned Parameters are Task Embeddings
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Disentangling Reasoning Capabilities from Language Models with
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157
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Zero-Shot Learners for Natural Language Understanding via a Unified
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Improving Semantic Matching through Dependency-Enhanced Pre-trained
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