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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
3D Serious Game Modeling and Design: Contributions to Language Learning ; Modélisation et Conception de jeu sérieux tridimensionnel : Contributions à l’apprentissage des langues
Tazouti, Yassine. - : HAL CCSD, 2021
In: https://hal.archives-ouvertes.fr/tel-03315793 ; Environnements Informatiques pour l'Apprentissage Humain. Université Ibn Tofail, Kénitra (Maroc), 2021. Français (2021)
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5
10D: Phonology, Morphology and Word Segmentation #1 ...
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6
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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7
19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 2 ...
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8
18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 1 ...
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9
The Match-Extend Serialization Algorithm in Multiprecedence ...
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10
Recognizing Reduplicated Forms: Finite-State Buffered Machines ...
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11
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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12
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.233 Abstract: Chinese spelling correction (CSC) is a task to detect and correct spelling errors in texts. CSC is essentially a linguistic problem, thus the ability of language understanding is crucial to this task. In this paper, we propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) for CSC, which jointly learns how to understand language and correct spelling errors. To this end, PLOME masks the chosen tokens with similar characters according to a confusion set rather than the fixed token ``[MASK]" as in BERT. Besides character prediction, PLOME also introduces pronunciation prediction to learn the misspelled knowledge on phonic level. Moreover, phonological and visual similarity knowledge is important to this task. PLOME utilizes GRU networks to model such knowledge based on characters' phonics and strokes. Experiments are conducted on widely used benchmarks. Our method achieves superior performance against state-of-the-art ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25563-plome-pre-training-with-misspelled-knowledge-for-chinese-spelling-correction
https://dx.doi.org/10.48448/hvyh-zh15
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13
A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images ...
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14
A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images ...
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15
Including Signed Languages in Natural Language Processing ...
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16
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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17
The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies ...
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18
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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19
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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20
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
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