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1
A Latent-Variable Model for Intrinsic Probing ...
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A Neighbourhood Framework for Resource-Lean Content Flagging ...
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3
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives ...
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4
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension ...
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5
Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ...
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6
Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training ...
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7
Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models? ...
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8
CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding ...
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9
How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs? ...
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10
Is Sparse Attention more Interpretable? ...
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11
Is Sparse Attention more Interpretable? ...
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12
Quantifying Gender Biases Towards Politicians on Reddit ...
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13
A Survey on Gender Bias in Natural Language Processing ...
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14
Semi-Supervised Exaggeration Detection of Health Science Press Releases ...
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15
Inducing Language-Agnostic Multilingual Representations ...
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16
Zero-Shot Cross-Lingual Transfer with Meta Learning ...
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17
SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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18
Generating Fact Checking Explanations ...
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19
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension ...
Abstract: Although the vast majority of knowledge bases KBs are heavily biased towards English, Wikipedias do cover very different topics in different languages. Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem. We show that by leveraging this resource it is possible to robustly transfer models cross-lingually and that multilingual support significantly improves (zero-shot) relation extraction, enabling the population of low-resourced KBs from their well-populated counterparts. ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1908.05111
https://arxiv.org/abs/1908.05111
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TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP ...
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