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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
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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5
The Match-Extend Serialization Algorithm in Multiprecedence ...
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6
Recognizing Reduplicated Forms: Finite-State Buffered Machines ...
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7
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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8
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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9
Including Signed Languages in Natural Language Processing ...
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10
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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11
The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies ...
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12
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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13
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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14
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
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15
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations ...
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16
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.2 Abstract: Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem. In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: “Are cows more likely to lie down the longer they stand?”). This challenging task has unique characteristics that make it particularly suitable for automatic learning. We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP. We use our models to identify potentially funny papers in a large dataset of over 630,000 articles. The results demonstrate the ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/y1w7-4z64
https://underline.io/lecture/25349-how-did-this-get-fundedquestion!-automatically-identifying-quirky-scientific-achievements
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17
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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18
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding ...
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19
Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy ...
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20
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation ...
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