-
Interspeech 2020: A Mask based Model for Mandarin Chinese Polyphone Disambiguation
A Mask based Model for Mandarin Chinese Polyphone Disambiguation
published: 09 Nov 2020
-
Cao Bao (disambiguation)
Cao Bao may refer to:
Cao Bao (Shutong) (曹褒; died 102), style name Shutong (叔通), Eastern Han Dynasty scholar. see Book of the Later Han
Cao Bao (died 196) (曹豹; died 196), vassal serving under the Eastern Han Dynasty warlord Tao Qian, later served Liu Bei and Lü Bu
Cao Bao (曹褒), Eastern Han Dynasty official, served as Administrator of Yingchuan, grandfather of Cao Ren
Source: https://en.wikipedia.org/wiki/Cao_Bao_(disambiguation)
Created with WikipediaReaderReborn (c) WikipediaReader
published: 01 Oct 2021
-
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (Research Paper Walkthrough)
#bert #wsd #wordnet
This research uses BERT for Word Sense Disambiguation use case in NLP by modeling the entire problem as sentence classification task using the Gloss knowledge. They show state-of-art results on benchmark datasets.
⏩ Abstract: Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based methods. Recent studies have shown the effectiveness of incorporating gloss (sense definition) into neural networks for WSD. However, compared with traditional word expert supervised methods, they have not achieved much improvement. In this paper, we focus on how to better leverage gloss knowledge in a...
published: 07 Apr 2021
-
Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (longer introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level ...
published: 03 Feb 2022
-
Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (3 minutes introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-lev...
published: 03 Feb 2022
-
Polyphone Disambiguition in Mandarin Chinese with Semi-Supervised Learning - (3 minutes introduc...
Title: Polyphone Disambiguition in Mandarin Chinese with Semi-Supervised Learning - (3 minutes introduction)
Authors: Yi Shi (Xmov, China), Congyi Wang (Xmov, China), Yu Chen (Xmov, China), Bin Wang (Xmov, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: The majority of Chinese characters are monophonic, while a special group of characters, called polyphonic characters, have multiple pronunciations. As a prerequisite of performing speech-related generative tasks, the correct pronunciation must be identified among several candidates. This process is called Polyphone Disambiguation. Although the problem has been well explored with both knowledge-based and learning-based approaches, it remains challenging due to the lack of publicly available l...
published: 03 Feb 2022
-
User Interaction Models for Disambiguation in Programming by Example
User Interaction Models for Disambiguation in Programming by Example
Mikaël Mayer, Gustavo Soares, Maxim Grechkin, Vu Le, Mark Marron, Oleksandr Polozov, Rishabh Singh, Ben Zorn, Sumit Gulwani
Abstract:
Programming by Examples (PBE) has the potential to revolutionize end-user programming by \ enabling end users, most of whom are non-programmers, to create small scripts for automating \ repetitive tasks. \ However, examples, though often easy to provide, are an ambiguous specification of the user's intent. \ Because of that, a key impedance in adoption of PBE systems is the lack of user confidence in the correctness of \ the program that was synthesized by the system. \ We present two novel user interaction models that communicate actionable information to the user to help resolve amb...
published: 25 Oct 2015
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Top 5 Disney TV Series of 2022
If you want to to know the greatest Disney TV Shows that came out 2022 you should definitely watch our picks for the best Disney TV Series of 2022. All Disney+ series in this ranking started in 2022 and are available on Disney plus.
Discover the best…
📹 YouTube tool: https://www.tubebuddy.com/communitv
We missed your favorite Disney TV Series of 2022? Let us know in the comments!
Follow us on...
Instagram: http://bit.ly/2rnljTB
Facebook: http://bit.ly/2PbYxGn
TV Series in this Ranking:
5. Moon Knight (2022): (00:10)
4. Obi-Wan Kenobi (2022): (00:47)
3. Star Wars: Tales of the Jedi (2022- ): (01:32)
2. Light & Magic (2022): (02:21)
1. Star Wars: Andor (2022- ): (03:57)
You want to work with us?
For collaboration requests please contact us via…
Mail: [email protected]
Music: www.ben...
published: 09 May 2023
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FNF hijacked transmission (VS Sonic.exe) So many Sonic's cover
The spirits:
https://fridaynightfunking.fandom.com/wiki/Sonic_the_Hedgehog_(disambiguation)
The FLP:
https://drive.google.com/drive/u/0/folders/1-_9IjB_GTb0bz-KRKuf_8bGsDDwJk09K
The chromatics:
https://drive.google.com/drive/u/0/folders/1DSzkaT0etPqB7uAXrhhbNBrsQdkyGlXQ
Credits:
Typho: chart
Erick Animations: all of the rerun's sprites and the bg
jesterfrog : song
RamenDominoes: cinematics script
Download:
https://www.mediafire.com/file/sfo4vpwp72ezx3a/RE-RUN-RECHART.zip/file
published: 01 Jun 2024
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Topic Modeling and Word Sense Disambiguation on the ...
"Topic Modeling and Word Sense Disambiguation on the Ancora corpus
".Rubén Izquierdo, Marten Postma, Piek Vossen
published: 22 Dec 2015
0:42
Cao Bao (disambiguation)
Cao Bao may refer to:
Cao Bao (Shutong) (曹褒; died 102), style name Shutong (叔通), Eastern Han Dynasty scholar. see Book of the Later Han
Cao Bao (died 196) (曹豹;...
Cao Bao may refer to:
Cao Bao (Shutong) (曹褒; died 102), style name Shutong (叔通), Eastern Han Dynasty scholar. see Book of the Later Han
Cao Bao (died 196) (曹豹; died 196), vassal serving under the Eastern Han Dynasty warlord Tao Qian, later served Liu Bei and Lü Bu
Cao Bao (曹褒), Eastern Han Dynasty official, served as Administrator of Yingchuan, grandfather of Cao Ren
Source: https://en.wikipedia.org/wiki/Cao_Bao_(disambiguation)
Created with WikipediaReaderReborn (c) WikipediaReader
https://wn.com/Cao_Bao_(Disambiguation)
Cao Bao may refer to:
Cao Bao (Shutong) (曹褒; died 102), style name Shutong (叔通), Eastern Han Dynasty scholar. see Book of the Later Han
Cao Bao (died 196) (曹豹; died 196), vassal serving under the Eastern Han Dynasty warlord Tao Qian, later served Liu Bei and Lü Bu
Cao Bao (曹褒), Eastern Han Dynasty official, served as Administrator of Yingchuan, grandfather of Cao Ren
Source: https://en.wikipedia.org/wiki/Cao_Bao_(disambiguation)
Created with WikipediaReaderReborn (c) WikipediaReader
- published: 01 Oct 2021
- views: 0
11:18
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (Research Paper Walkthrough)
#bert #wsd #wordnet
This research uses BERT for Word Sense Disambiguation use case in NLP by modeling the entire problem as sentence classification task using t...
#bert #wsd #wordnet
This research uses BERT for Word Sense Disambiguation use case in NLP by modeling the entire problem as sentence classification task using the Gloss knowledge. They show state-of-art results on benchmark datasets.
⏩ Abstract: Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based methods. Recent studies have shown the effectiveness of incorporating gloss (sense definition) into neural networks for WSD. However, compared with traditional word expert supervised methods, they have not achieved much improvement. In this paper, we focus on how to better leverage gloss knowledge in a supervised neural WSD system. We construct context-gloss pairs and propose three BERT-based models for WSD. We fine-tune the pre-trained BERT model on SemCor3.0 training corpus and the experimental results on several English all-words WSD benchmark datasets show that our approach outperforms the state-of-the-art systems.
Please feel free to share out the content and subscribe to my channel :)
⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1
⏩ OUTLINE:
0:00 - Abstract
01:46 - Task Definition
02:11 - Data Collection approach
02:30 - WordNet Overview
03:35 - Sentence construction method table overview
05:27 - BERT(Token-CLS)
06:41 - GlossBERT
07:52 - Context-Gloss Pair with Weak Supervision
08:55 - GlossBERT(Token-CLS)
09:20 - GlossBERT(Sent-CLS)
09:44 - GlossBERT(Sent-CLS-WS)
10:09 - Results
⏩ Paper Title: GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
⏩ Paper: https://arxiv.org/abs/1908.07245v4
⏩ Code: https://github.com/HSLCY/GlossBERT
⏩ Author: Luyao Huang, Chi Sun, Xipeng Qiu, Xuanjing Huang
⏩ Organisation: Fudan University
⏩ IMPORTANT LINKS
Full Playlist on BERT usecases in NLP: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Text Data Augmentation Techniques: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ
Full Playlist on Text Summarization: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf
Full Playlist on Evaluating NLG Systems: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu
*********************************************
If you want to support me financially which totally optional and voluntary :) ❤️
You can consider buying me chai ( because i don't drink coffee :) ) at https://www.buymeacoffee.com/TechvizCoffee
*********************************************
⏩ Youtube - https://www.youtube.com/c/TechVizTheDataScienceGuy
⏩ Blog - https://prakhartechviz.blogspot.com
⏩ LinkedIn - https://linkedin.com/in/prakhar21
⏩ Medium - https://medium.com/@prakhar.mishra
⏩ GitHub - https://github.com/prakhar21
⏩ Twitter - https://twitter.com/rattller
*********************************************
Please feel free to share out the content and subscribe to my channel :)
⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1
Tools I use for making videos :)
⏩ iPad - https://tinyurl.com/y39p6pwc
⏩ Apple Pencil - https://tinyurl.com/y5rk8txn
⏩ GoodNotes - https://tinyurl.com/y627cfsa
#techviz #datascienceguy #ai #researchpaper #naturallanguageprocessing #bart
https://wn.com/Glossbert_Bert_For_Word_Sense_Disambiguation_With_Gloss_Knowledge_(Research_Paper_Walkthrough)
#bert #wsd #wordnet
This research uses BERT for Word Sense Disambiguation use case in NLP by modeling the entire problem as sentence classification task using the Gloss knowledge. They show state-of-art results on benchmark datasets.
⏩ Abstract: Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based methods. Recent studies have shown the effectiveness of incorporating gloss (sense definition) into neural networks for WSD. However, compared with traditional word expert supervised methods, they have not achieved much improvement. In this paper, we focus on how to better leverage gloss knowledge in a supervised neural WSD system. We construct context-gloss pairs and propose three BERT-based models for WSD. We fine-tune the pre-trained BERT model on SemCor3.0 training corpus and the experimental results on several English all-words WSD benchmark datasets show that our approach outperforms the state-of-the-art systems.
Please feel free to share out the content and subscribe to my channel :)
⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1
⏩ OUTLINE:
0:00 - Abstract
01:46 - Task Definition
02:11 - Data Collection approach
02:30 - WordNet Overview
03:35 - Sentence construction method table overview
05:27 - BERT(Token-CLS)
06:41 - GlossBERT
07:52 - Context-Gloss Pair with Weak Supervision
08:55 - GlossBERT(Token-CLS)
09:20 - GlossBERT(Sent-CLS)
09:44 - GlossBERT(Sent-CLS-WS)
10:09 - Results
⏩ Paper Title: GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
⏩ Paper: https://arxiv.org/abs/1908.07245v4
⏩ Code: https://github.com/HSLCY/GlossBERT
⏩ Author: Luyao Huang, Chi Sun, Xipeng Qiu, Xuanjing Huang
⏩ Organisation: Fudan University
⏩ IMPORTANT LINKS
Full Playlist on BERT usecases in NLP: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Text Data Augmentation Techniques: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ
Full Playlist on Text Summarization: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf
Full Playlist on Evaluating NLG Systems: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu
*********************************************
If you want to support me financially which totally optional and voluntary :) ❤️
You can consider buying me chai ( because i don't drink coffee :) ) at https://www.buymeacoffee.com/TechvizCoffee
*********************************************
⏩ Youtube - https://www.youtube.com/c/TechVizTheDataScienceGuy
⏩ Blog - https://prakhartechviz.blogspot.com
⏩ LinkedIn - https://linkedin.com/in/prakhar21
⏩ Medium - https://medium.com/@prakhar.mishra
⏩ GitHub - https://github.com/prakhar21
⏩ Twitter - https://twitter.com/rattller
*********************************************
Please feel free to share out the content and subscribe to my channel :)
⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1
Tools I use for making videos :)
⏩ iPad - https://tinyurl.com/y39p6pwc
⏩ Apple Pencil - https://tinyurl.com/y5rk8txn
⏩ GoodNotes - https://tinyurl.com/y627cfsa
#techviz #datascienceguy #ai #researchpaper #naturallanguageprocessing #bart
- published: 07 Apr 2021
- views: 2054
14:55
Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (longer introduction)
Authors: Ju...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (longer introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level information in order to get meaningful context extraction. Particularly, we employ a pre-trained bidirectional encoder from Transformers (BERT) model to extract character-level features, and an external Chinese word segmentation (CWS) tool is used to obtain the word units. We adopt a mixed pooling mechanism to convert character-level features into word-level features based on the segmentation results. A window-based attention module is utilized to incorporate contextual word-level features for the polyphonic characters. Experimental results show that our method achieves an accuracy of 99.06% on an open benchmark dataset for Mandarin Chinese polyphone disambiguation, which outperforms the baseline systems.
For more details and PDF version of the paper visit: https://www.isca-speech.org/archive/interspeech_2021/li21n_interspeech.html
d04s07t04lng
https://wn.com/Improving_Polyphone_Disambiguation_For_Mandarin_Chinese_By_Combining_Mix_Pooling_Strategy_And_Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (longer introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level information in order to get meaningful context extraction. Particularly, we employ a pre-trained bidirectional encoder from Transformers (BERT) model to extract character-level features, and an external Chinese word segmentation (CWS) tool is used to obtain the word units. We adopt a mixed pooling mechanism to convert character-level features into word-level features based on the segmentation results. A window-based attention module is utilized to incorporate contextual word-level features for the polyphonic characters. Experimental results show that our method achieves an accuracy of 99.06% on an open benchmark dataset for Mandarin Chinese polyphone disambiguation, which outperforms the baseline systems.
For more details and PDF version of the paper visit: https://www.isca-speech.org/archive/interspeech_2021/li21n_interspeech.html
d04s07t04lng
- published: 03 Feb 2022
- views: 10
3:03
Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (3 minutes introduction)
Authors:...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (3 minutes introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level information in order to get meaningful context extraction. Particularly, we employ a pre-trained bidirectional encoder from Transformers (BERT) model to extract character-level features, and an external Chinese word segmentation (CWS) tool is used to obtain the word units. We adopt a mixed pooling mechanism to convert character-level features into word-level features based on the segmentation results. A window-based attention module is utilized to incorporate contextual word-level features for the polyphonic characters. Experimental results show that our method achieves an accuracy of 99.06% on an open benchmark dataset for Mandarin Chinese polyphone disambiguation, which outperforms the baseline systems.
For more details and PDF version of the paper visit: https://www.isca-speech.org/archive/interspeech_2021/li21n_interspeech.html
d04s07t04trim
https://wn.com/Improving_Polyphone_Disambiguation_For_Mandarin_Chinese_By_Combining_Mix_Pooling_Strategy_And_Wi...
Title: Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-pooling Strategy and Window-based Attention - (3 minutes introduction)
Authors: Junjie Li (Ping An Technology, China), Zhiyu Zhang (National Tsing Hua University, Taiwan), Minchuan Chen (Ping An Technology, China), Jun Ma (Ping An Technology, China), Shaojun Wang (Ping An Technology, China), Jing Xiao (Ping An Technology, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: In this paper, we propose a novel system based on word-level features and window-based attention for polyphone disambiguation, which is a fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin Chinese. The framework aims to combine a pre-trained language model with explicit word-level information in order to get meaningful context extraction. Particularly, we employ a pre-trained bidirectional encoder from Transformers (BERT) model to extract character-level features, and an external Chinese word segmentation (CWS) tool is used to obtain the word units. We adopt a mixed pooling mechanism to convert character-level features into word-level features based on the segmentation results. A window-based attention module is utilized to incorporate contextual word-level features for the polyphonic characters. Experimental results show that our method achieves an accuracy of 99.06% on an open benchmark dataset for Mandarin Chinese polyphone disambiguation, which outperforms the baseline systems.
For more details and PDF version of the paper visit: https://www.isca-speech.org/archive/interspeech_2021/li21n_interspeech.html
d04s07t04trim
- published: 03 Feb 2022
- views: 8
3:06
Polyphone Disambiguition in Mandarin Chinese with Semi-Supervised Learning - (3 minutes introduc...
Title: Polyphone Disambiguition in Mandarin Chinese with Semi-Supervised Learning - (3 minutes introduction)
Authors: Yi Shi (Xmov, China), Congyi Wang (Xmov, ...
Title: Polyphone Disambiguition in Mandarin Chinese with Semi-Supervised Learning - (3 minutes introduction)
Authors: Yi Shi (Xmov, China), Congyi Wang (Xmov, China), Yu Chen (Xmov, China), Bin Wang (Xmov, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: The majority of Chinese characters are monophonic, while a special group of characters, called polyphonic characters, have multiple pronunciations. As a prerequisite of performing speech-related generative tasks, the correct pronunciation must be identified among several candidates. This process is called Polyphone Disambiguation. Although the problem has been well explored with both knowledge-based and learning-based approaches, it remains challenging due to the lack of publicly available labeled datasets and the irregular nature of polyphone in Mandarin Chinese. In this paper, we propose a novel semi-supervised learning (SSL) framework for Mandarin Chinese polyphone disambiguation that can potentially leverage unlimited unlabeled text data. We explore the effect of various proxy labeling strategies including entropy-thresholding and lexicon-based labeling. Qualitative and quantitative experiments demonstrate that our method achieves state-of-the-art performance. In addition, we publish a novel dataset specifically for the polyphone disambiguation task to promote further researches.
For more details and PDF version of the paper visit: https://www.isca-speech.org/archive/interspeech_2021/shi21d_interspeech.html
d04s07t05trim
https://wn.com/Polyphone_Disambiguition_In_Mandarin_Chinese_With_Semi_Supervised_Learning_(3_Minutes_Introduc...
Title: Polyphone Disambiguition in Mandarin Chinese with Semi-Supervised Learning - (3 minutes introduction)
Authors: Yi Shi (Xmov, China), Congyi Wang (Xmov, China), Yu Chen (Xmov, China), Bin Wang (Xmov, China)
Category: Speech Synthesis: Linguistic processing, paradigms and other topics
Abstract: The majority of Chinese characters are monophonic, while a special group of characters, called polyphonic characters, have multiple pronunciations. As a prerequisite of performing speech-related generative tasks, the correct pronunciation must be identified among several candidates. This process is called Polyphone Disambiguation. Although the problem has been well explored with both knowledge-based and learning-based approaches, it remains challenging due to the lack of publicly available labeled datasets and the irregular nature of polyphone in Mandarin Chinese. In this paper, we propose a novel semi-supervised learning (SSL) framework for Mandarin Chinese polyphone disambiguation that can potentially leverage unlimited unlabeled text data. We explore the effect of various proxy labeling strategies including entropy-thresholding and lexicon-based labeling. Qualitative and quantitative experiments demonstrate that our method achieves state-of-the-art performance. In addition, we publish a novel dataset specifically for the polyphone disambiguation task to promote further researches.
For more details and PDF version of the paper visit: https://www.isca-speech.org/archive/interspeech_2021/shi21d_interspeech.html
d04s07t05trim
- published: 03 Feb 2022
- views: 12
0:30
User Interaction Models for Disambiguation in Programming by Example
User Interaction Models for Disambiguation in Programming by Example
Mikaël Mayer, Gustavo Soares, Maxim Grechkin, Vu Le, Mark Marron, Oleksandr Polozov, Rishab...
User Interaction Models for Disambiguation in Programming by Example
Mikaël Mayer, Gustavo Soares, Maxim Grechkin, Vu Le, Mark Marron, Oleksandr Polozov, Rishabh Singh, Ben Zorn, Sumit Gulwani
Abstract:
Programming by Examples (PBE) has the potential to revolutionize end-user programming by \ enabling end users, most of whom are non-programmers, to create small scripts for automating \ repetitive tasks. \ However, examples, though often easy to provide, are an ambiguous specification of the user's intent. \ Because of that, a key impedance in adoption of PBE systems is the lack of user confidence in the correctness of \ the program that was synthesized by the system. \ We present two novel user interaction models that communicate actionable information to the user to help resolve ambiguity in the examples. \ One of these models allows the user to effectively navigate between the huge set of \ programs that are consistent with the examples provided by the user. \ The other model uses active learning to ask directed example-based questions to the user on the test input data over \ which the user intends to run the synthesized program. \ Our user studies show that \ each of these models significantly reduces the number of errors in the performed task without any difference in completion time. \ Moreover, both models are perceived as useful, \ and the proactive active-learning based model has a slightly higher preference regarding the users' confidence in the result.
ACM DL: http://dl.acm.org/citation.cfm?id=2807459
DOI: http://dx.doi.org/10.1145/2807442.2807459
https://wn.com/User_Interaction_Models_For_Disambiguation_In_Programming_By_Example
User Interaction Models for Disambiguation in Programming by Example
Mikaël Mayer, Gustavo Soares, Maxim Grechkin, Vu Le, Mark Marron, Oleksandr Polozov, Rishabh Singh, Ben Zorn, Sumit Gulwani
Abstract:
Programming by Examples (PBE) has the potential to revolutionize end-user programming by \ enabling end users, most of whom are non-programmers, to create small scripts for automating \ repetitive tasks. \ However, examples, though often easy to provide, are an ambiguous specification of the user's intent. \ Because of that, a key impedance in adoption of PBE systems is the lack of user confidence in the correctness of \ the program that was synthesized by the system. \ We present two novel user interaction models that communicate actionable information to the user to help resolve ambiguity in the examples. \ One of these models allows the user to effectively navigate between the huge set of \ programs that are consistent with the examples provided by the user. \ The other model uses active learning to ask directed example-based questions to the user on the test input data over \ which the user intends to run the synthesized program. \ Our user studies show that \ each of these models significantly reduces the number of errors in the performed task without any difference in completion time. \ Moreover, both models are perceived as useful, \ and the proactive active-learning based model has a slightly higher preference regarding the users' confidence in the result.
ACM DL: http://dl.acm.org/citation.cfm?id=2807459
DOI: http://dx.doi.org/10.1145/2807442.2807459
- published: 25 Oct 2015
- views: 839
4:48
Top 5 Disney TV Series of 2022
If you want to to know the greatest Disney TV Shows that came out 2022 you should definitely watch our picks for the best Disney TV Series of 2022. All Disney+ ...
If you want to to know the greatest Disney TV Shows that came out 2022 you should definitely watch our picks for the best Disney TV Series of 2022. All Disney+ series in this ranking started in 2022 and are available on Disney plus.
Discover the best…
📹 YouTube tool: https://www.tubebuddy.com/communitv
We missed your favorite Disney TV Series of 2022? Let us know in the comments!
Follow us on...
Instagram: http://bit.ly/2rnljTB
Facebook: http://bit.ly/2PbYxGn
TV Series in this Ranking:
5. Moon Knight (2022): (00:10)
4. Obi-Wan Kenobi (2022): (00:47)
3. Star Wars: Tales of the Jedi (2022- ): (01:32)
2. Light & Magic (2022): (02:21)
1. Star Wars: Andor (2022- ): (03:57)
You want to work with us?
For collaboration requests please contact us via…
Mail:
[email protected]
Music: www.bensound.com
https://wn.com/Top_5_Disney_Tv_Series_Of_2022
If you want to to know the greatest Disney TV Shows that came out 2022 you should definitely watch our picks for the best Disney TV Series of 2022. All Disney+ series in this ranking started in 2022 and are available on Disney plus.
Discover the best…
📹 YouTube tool: https://www.tubebuddy.com/communitv
We missed your favorite Disney TV Series of 2022? Let us know in the comments!
Follow us on...
Instagram: http://bit.ly/2rnljTB
Facebook: http://bit.ly/2PbYxGn
TV Series in this Ranking:
5. Moon Knight (2022): (00:10)
4. Obi-Wan Kenobi (2022): (00:47)
3. Star Wars: Tales of the Jedi (2022- ): (01:32)
2. Light & Magic (2022): (02:21)
1. Star Wars: Andor (2022- ): (03:57)
You want to work with us?
For collaboration requests please contact us via…
Mail:
[email protected]
Music: www.bensound.com
- published: 09 May 2023
- views: 29724
3:53
FNF hijacked transmission (VS Sonic.exe) So many Sonic's cover
The spirits:
https://fridaynightfunking.fandom.com/wiki/Sonic_the_Hedgehog_(disambiguation)
The FLP:
https://drive.google.com/drive/u/0/folders/1-_9IjB_GTb0bz-K...
The spirits:
https://fridaynightfunking.fandom.com/wiki/Sonic_the_Hedgehog_(disambiguation)
The FLP:
https://drive.google.com/drive/u/0/folders/1-_9IjB_GTb0bz-KRKuf_8bGsDDwJk09K
The chromatics:
https://drive.google.com/drive/u/0/folders/1DSzkaT0etPqB7uAXrhhbNBrsQdkyGlXQ
Credits:
Typho: chart
Erick Animations: all of the rerun's sprites and the bg
jesterfrog : song
RamenDominoes: cinematics script
Download:
https://www.mediafire.com/file/sfo4vpwp72ezx3a/RE-RUN-RECHART.zip/file
https://wn.com/Fnf_Hijacked_Transmission_(Vs_Sonic.Exe)_So_Many_Sonic's_Cover
The spirits:
https://fridaynightfunking.fandom.com/wiki/Sonic_the_Hedgehog_(disambiguation)
The FLP:
https://drive.google.com/drive/u/0/folders/1-_9IjB_GTb0bz-KRKuf_8bGsDDwJk09K
The chromatics:
https://drive.google.com/drive/u/0/folders/1DSzkaT0etPqB7uAXrhhbNBrsQdkyGlXQ
Credits:
Typho: chart
Erick Animations: all of the rerun's sprites and the bg
jesterfrog : song
RamenDominoes: cinematics script
Download:
https://www.mediafire.com/file/sfo4vpwp72ezx3a/RE-RUN-RECHART.zip/file
- published: 01 Jun 2024
- views: 473
29:06
Topic Modeling and Word Sense Disambiguation on the ...
"Topic Modeling and Word Sense Disambiguation on the Ancora corpus
".Rubén Izquierdo, Marten Postma, Piek Vossen
"Topic Modeling and Word Sense Disambiguation on the Ancora corpus
".Rubén Izquierdo, Marten Postma, Piek Vossen
https://wn.com/Topic_Modeling_And_Word_Sense_Disambiguation_On_The_...
"Topic Modeling and Word Sense Disambiguation on the Ancora corpus
".Rubén Izquierdo, Marten Postma, Piek Vossen
- published: 22 Dec 2015
- views: 366