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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
Formalization of AMR Inference via Hybrid Logic Tableaux ...
Goldner, Eli Tecumseh. - : Brandeis University, 2022
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5
Chinese character decomposition for neural MT with multi-word expressions
In: Han, Lifeng orcid:0000-0002-3221-2185 , Jones, Gareth J.F. orcid:0000-0003-2923-8365 , Smeaton, Alan F. orcid:0000-0003-1028-8389 and Bolzoni, Paolo (2021) Chinese character decomposition for neural MT with multi-word expressions. In: 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021), 31 May- 2 June 2021, Reykjavik, Iceland (Online). (In Press) (2021)
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6
Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing ...
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7
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation ...
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8
10D: Phonology, Morphology and Word Segmentation #1 ...
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9
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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10
19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 2 ...
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11
18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 1 ...
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12
SpeakEasy Pronunciation Trainer: Personalized Multimodal Pronunciation Training ...
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13
The Match-Extend Serialization Algorithm in Multiprecedence ...
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14
Recognizing Reduplicated Forms: Finite-State Buffered Machines ...
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15
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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16
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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17
SpeakEasy Pronunciation Trainer: Personalized Multimodal Pronunciation Training ...
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18
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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19
Quantification: the view from natural language generation ...
Carstensen, Kai-Uwe. - : Universitätsbibliothek Siegen, 2021
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20
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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