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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 ...
Abstract: The primary goals of computer-assisted pronunciation training (CAPT) systems are to provide a personalized interactive environment and to accurately diagnose mispronunciations. Automatic speech recognition (ASR) systems have been shown to be an effective tool for diagnosing mispronunciations. While the data ASR systems output can be difficult for the layperson to understand, presenting it in a multimodal fashion can make it easier and feeding it into an automated narrative system can produce personalized feedback. In the absence of native speech examples, synthetic examples produced by text-to-speech (TTS) engines have proven to be an adequate substitute, making data collection easier and allowing for larger CAPT systems. In this work we present the SpeakEasy pronunciation trainer, a CAPT system that leverages ASR, TTS, automated narrative systems, and multimodal data representation to provide a personalized interactive environment that tracks a user's progress over time. ...
Keyword: Cognitive Linguistics; Cognitive Science; Computational Intelligence; Computational Linguistics; E-Learning; Phonetics; Phonology; Semantics
URL: https://dx.doi.org/10.48448/aa25-n461
https://underline.io/lecture/26896-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 ...
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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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