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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
Tripartitions of the first person space (English speakers, Condition 1) ...
Maldonado, Mora. - : Open Science Framework, 2022
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5
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation ...
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6
SpeakEasy Pronunciation Trainer: Personalized Multimodal Pronunciation Training ...
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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
SpeakEasy Pronunciation Trainer: Personalized Multimodal Pronunciation Training ...
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10
Tripartitions of the first person space (Tamil speakers, Condition 1) ...
Maldonado, Mora. - : Open Science Framework, 2021
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11
Including Signed Languages in Natural Language Processing ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.570 Abstract: Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of ...
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/x502-5y64
https://underline.io/lecture/25681-including-signed-languages-in-natural-language-processing
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12
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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13
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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14
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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15
HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation ...
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16
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors ...
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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 ...
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