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1
Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ...
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2
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse​ ...
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3
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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4
10D: Phonology, Morphology and Word Segmentation #1 ...
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5
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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6
19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 2 ...
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18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 1 ...
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8
The Match-Extend Serialization Algorithm in Multiprecedence ...
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9
Recognizing Reduplicated Forms: Finite-State Buffered Machines ...
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10
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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11
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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12
Including Signed Languages in Natural Language Processing ...
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13
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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14
The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies ...
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15
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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16
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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17
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
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18
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations ...
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19
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements ...
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20
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.106 Abstract: Model-agnostic meta-learning (MAML) has been recently put forth as a strategy to learn resource-poor languages in a sample-efficient fashion. Nevertheless, the properties of these languages are often not well represented by those available during training. Hence, we argue that the i.i.d. assumption ingrained in MAML makes it ill-suited for cross-lingual NLP. In fact, under a decision-theoretic framework, MAML can be interpreted as minimising the expected risk across training languages (with a uniform prior), which is known as Bayes criterion. To increase its robustness to outlier languages, we create two variants of MAML based on alternative criteria: Minimax MAML reduces the maximum risk across languages, while Neyman–Pearson MAML constrains the risk in each language to a maximum threshold. Both criteria constitute fully differentiable two-player games. In light of this, we propose a new adaptive optimiser solving for a local ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/26197-minimax-and-neyman-pearson-meta-learning-for-outlier-languages
https://dx.doi.org/10.48448/zydv-7c20
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