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156 results on '"Bao, Feilong"'

1. MCDubber: Multimodal Context-Aware Expressive Video Dubbing

Author

Zhao, Yuan, Jia, Zhenqi, Liu, Rui, Hu, De, Bao, Feilong, and Gao, Guanglai

Subjects
Computer Science - Multimedia, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Automatic Video Dubbing (AVD) aims to take the given script and generate speech that aligns with lip motion and prosody expressiveness. Current AVD models mainly utilize visual information of the current sentence to enhance the prosody of synthesized speech. However, it is crucial to consider whether the prosody of the generated dubbing aligns with the multimodal context, as the dubbing will be combined with the original context in the final video. This aspect has been overlooked in previous studies. To address this issue, we propose a Multimodal Context-aware video Dubbing model, termed \textbf{MCDubber}, to convert the modeling object from a single sentence to a longer sequence with context information to ensure the consistency of the global context prosody. MCDubber comprises three main components: (1) A context duration aligner aims to learn the context-aware alignment between the text and lip frames; (2) A context prosody predictor seeks to read the global context visual sequence and predict the context-aware global energy and pitch; (3) A context acoustic decoder ultimately predicts the global context mel-spectrogram with the assistance of adjacent ground-truth mel-spectrograms of the target sentence. Through this process, MCDubber fully considers the influence of multimodal context on the prosody expressiveness of the current sentence when dubbing. The extracted mel-spectrogram belonging to the target sentence from the output context mel-spectrograms is the final required dubbing audio. Extensive experiments on the Chem benchmark dataset demonstrate that our MCDubber significantly improves dubbing expressiveness compared to all advanced baselines. The code and demos are available at https://github.com/XiaoYuanJun-zy/MCDubber., Comment: Accepted by NCMMSC2024

Published
2024

2. L^2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification

Author

Liang, Qiuyu, Wang, Weihua, Bao, Feilong, and Gao, Guanglai

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations in Euclidean space, which do not explicitly capture the tree-like hierarchical structure exhibited in real-world datasets that modeled as graphs. In this paper, we attempt to introduce hyperbolic space into linear GCN and propose a novel framework for Lorentzian linear GCN. Specifically, we map the learned features of graph nodes into hyperbolic space, and then perform a Lorentzian linear feature transformation to capture the underlying tree-like structure of data. Experimental results on standard citation networks datasets with semi-supervised learning show that our approach yields new state-of-the-art results of accuracy 74.7$\%$ on Citeseer and 81.3$\%$ on PubMed datasets. Furthermore, we observe that our approach can be trained up to two orders of magnitude faster than other nonlinear GCN models on PubMed dataset. Our code is publicly available at https://github.com/llqy123/LLGC-master., Comment: Accepted by LREC-COLING 2024

Published
2024

4. MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset

Author

Liang, Kailin, Liu, Bin, Hu, Yifan, Liu, Rui, Bao, Feilong, and Gao, Guanglai

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Text-to-Speech (TTS) synthesis for low-resource languages is an attractive research issue in academia and industry nowadays. Mongolian is the official language of the Inner Mongolia Autonomous Region and a representative low-resource language spoken by over 10 million people worldwide. However, there is a relative lack of open-source datasets for Mongolian TTS. Therefore, we make public an open-source multi-speaker Mongolian TTS dataset, named MnTTS2, for the benefit of related researchers. In this work, we prepare the transcription from various topics and invite three professional Mongolian announcers to form a three-speaker TTS dataset, in which each announcer records 10 hours of speeches in Mongolian, resulting 30 hours in total. Furthermore, we build the baseline system based on the state-of-the-art FastSpeech2 model and HiFi-GAN vocoder. The experimental results suggest that the constructed MnTTS2 dataset is sufficient to build robust multi-speaker TTS models for real-world applications. The MnTTS2 dataset, training recipe, and pretrained models are released at: \url{https://github.com/ssmlkl/MnTTS2}, Comment: Accepted by NCMMSC'2022 (https://ncmmsc2022.ustc.edu.cn/main.htm)

Published
2022

5. STAR: Syntax- and Topic-Aware Role Dialogue Summarization

Author

Shi, Jiangyuan, Zhang, Fujun, Gao, Zhenjie, Bao, Feilong, Gao, Guanglai, Zhang, Wenjun, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Si, Zhanjun, editor, and Zhang, Qinhu, editor

Published
2024
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6. Few-Shot Table-to-Text Generation with Structural Bias Attention

Author

Liu, Di, Wang, Weihua, Bao, Feilong, Gaov, Guanglai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Fenrong, editor, Sadanandan, Arun Anand, editor, Pham, Duc Nghia, editor, Mursanto, Petrus, editor, and Lukose, Dickson, editor

Published
2024
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7. MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline

Author

Hu, Yifan, Yin, Pengkai, Liu, Rui, Bao, Feilong, and Gao, Guanglai

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

This paper introduces a high-quality open-source text-to-speech (TTS) synthesis dataset for Mongolian, a low-resource language spoken by over 10 million people worldwide. The dataset, named MnTTS, consists of about 8 hours of transcribed audio recordings spoken by a 22-year-old professional female Mongolian announcer. It is the first publicly available dataset developed to promote Mongolian TTS applications in both academia and industry. In this paper, we share our experience by describing the dataset development procedures and faced challenges. To demonstrate the reliability of our dataset, we built a powerful non-autoregressive baseline system based on FastSpeech2 model and HiFi-GAN vocoder, and evaluated it using the subjective mean opinion score (MOS) and real time factor (RTF) metrics. Evaluation results show that the powerful baseline system trained on our dataset achieves MOS above 4 and RTF about $3.30\times10^{-1}$, which makes it applicable for practical use. The dataset, training recipe, and pretrained TTS models are freely available \footnote{\label{github}\url{https://github.com/walker-hyf/MnTTS}}., Comment: Accepted at the 2022 International Conference on Asian Language Processing (IALP2022)

Published
2022

11. TableSF: A Structural Bias Framework for Table-To-Text Generation

Author

Liu, Di, Wang, Weihua, Bao, Feilong, Gao, Guanglai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Iliadis, Lazaros, editor, Papaleonidas, Antonios, editor, Angelov, Plamen, editor, and Jayne, Chrisina, editor

Published
2023
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13. A Deep Investigation of RNN and Self-attention for the Cyrillic-Traditional Mongolian Bidirectional Conversion

Author

Na, Muhan, Liu, Rui, Bao, Feilong, Gao, Guanglai, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tanveer, Mohammad, editor, Agarwal, Sonali, editor, Ozawa, Seiichi, editor, Ekbal, Asif, editor, and Jatowt, Adam, editor

Published
2023
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14. Modeling Prosodic Phrasing with Multi-Task Learning in Tacotron-based TTS

Author

Liu, Rui, Sisman, Berrak, Bao, Feilong, Gao, Guanglai, and Li, Haizhou

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Computation and Language, Computer Science - Sound
Abstract

Tacotron-based end-to-end speech synthesis has shown remarkable voice quality. However, the rendering of prosody in the synthesized speech remains to be improved, especially for long sentences, where prosodic phrasing errors can occur frequently. In this paper, we extend the Tacotron-based speech synthesis framework to explicitly model the prosodic phrase breaks. We propose a multi-task learning scheme for Tacotron training, that optimizes the system to predict both Mel spectrum and phrase breaks. To our best knowledge, this is the first implementation of multi-task learning for Tacotron based TTS with a prosodic phrasing model. Experiments show that our proposed training scheme consistently improves the voice quality for both Chinese and Mongolian systems., Comment: To appear in IEEE Signal Processing Letters (SPL)

Published
2020
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15. WaveTTS: Tacotron-based TTS with Joint Time-Frequency Domain Loss

Author

Liu, Rui, Sisman, Berrak, Bao, Feilong, Gao, Guanglai, and Li, Haizhou

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Computation and Language, Computer Science - Sound
Abstract

Tacotron-based text-to-speech (TTS) systems directly synthesize speech from text input. Such frameworks typically consist of a feature prediction network that maps character sequences to frequency-domain acoustic features, followed by a waveform reconstruction algorithm or a neural vocoder that generates the time-domain waveform from acoustic features. As the loss function is usually calculated only for frequency-domain acoustic features, that doesn't directly control the quality of the generated time-domain waveform. To address this problem, we propose a new training scheme for Tacotron-based TTS, referred to as WaveTTS, that has 2 loss functions: 1) time-domain loss, denoted as the waveform loss, that measures the distortion between the natural and generated waveform; and 2) frequency-domain loss, that measures the Mel-scale acoustic feature loss between the natural and generated acoustic features. WaveTTS ensures both the quality of the acoustic features and the resulting speech waveform. To our best knowledge, this is the first implementation of Tacotron with joint time-frequency domain loss. Experimental results show that the proposed framework outperforms the baselines and achieves high-quality synthesized speech., Comment: To appear at Odyssey 2020, Tokyo, Japan

Published
2020

16. Teacher-Student Training for Robust Tacotron-based TTS

Author

Liu, Rui, Sisman, Berrak, Li, Jingdong, Bao, Feilong, Gao, Guanglai, and Li, Haizhou

Subjects
Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

While neural end-to-end text-to-speech (TTS) is superior to conventional statistical methods in many ways, the exposure bias problem in the autoregressive models remains an issue to be resolved. The exposure bias problem arises from the mismatch between the training and inference process, that results in unpredictable performance for out-of-domain test data at run-time. To overcome this, we propose a teacher-student training scheme for Tacotron-based TTS by introducing a distillation loss function in addition to the feature loss function. We first train a Tacotron2-based TTS model by always providing natural speech frames to the decoder, that serves as a teacher model. We then train another Tacotron2-based model as a student model, of which the decoder takes the predicted speech frames as input, similar to how the decoder works during run-time inference. With the distillation loss, the student model learns the output probabilities from the teacher model, that is called knowledge distillation. Experiments show that our proposed training scheme consistently improves the voice quality for out-of-domain test data both in Chinese and English systems., Comment: To appear at ICASSP2020, Barcelona, Spain

Published
2019

17. Interactive Mongolian Question Answer Matching Model Based on Attention Mechanism in the Law Domain

Author

Peng, Yutao, Wang, Weihua, Bao, Feilong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Liu, Yang, editor, Che, Wanxiang, editor, Feng, Yang, editor, Qiu, Xipeng, editor, Rao, Gaoqi, editor, and Chen, Yubo, editor

Published
2022
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18. End-to-End Large-Scale Image Retrieval Network with Convolution and Vision Transformers

Author

Zhang, Qing, Bao, Feilong, Su, Xiangdong, Wang, Weihua, Gao, Guanglai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pimenidis, Elias, editor, Angelov, Plamen, editor, Jayne, Chrisina, editor, Papaleonidas, Antonios, editor, and Aydin, Mehmet, editor

Published
2022
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19. Soft-BAC: Soft Bidirectional Alignment Cost for End-to-End Automatic Speech Recognition

Author

Wang, Yonghe, Zhang, Hui, Bao, Feilong, Gao, Guanglai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pham, Duc Nghia, editor, Theeramunkong, Thanaruk, editor, Governatori, Guido, editor, and Liu, Fenrong, editor

Published
2021
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20. Panoptic-DLA: Document Layout Analysis of Historical Newspapers Based on Proposal-Free Panoptic Segmentation Model

Author

Lu, Min, Bao, Feilong, Gao, Guanglai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Qiu, Han, editor, Zhang, Cheng, editor, Fei, Zongming, editor, Qiu, Meikang, editor, and Kung, Sun-Yuan, editor

Published
2021
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22. Mongolian Questions Classification Based on Multi-Head Attention

Author

Wang, Guangyi, Bao, Feilong, Wang, Weihua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Li, Sujian, editor, Zhang, Yue, editor, Liu, Yang, editor, He, Shizhu, editor, and Rao, Gaoqi, editor

Published
2020
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23. L$^2$GC: Lorentzian Linear Graph Convolutional Networks For Node Classification

Author

Liang, Qiuyu, Wang, Weihua, Bao, Feilong, Gao, Guanglai, Liang, Qiuyu, Wang, Weihua, Bao, Feilong, and Gao, Guanglai

Abstract

Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations in Euclidean space, which do not explicitly capture the tree-like hierarchical structure exhibited in real-world datasets that modeled as graphs. In this paper, we attempt to introduce hyperbolic space into linear GCN and propose a novel framework for Lorentzian linear GCN. Specifically, we map the learned features of graph nodes into hyperbolic space, and then perform a Lorentzian linear feature transformation to capture the underlying tree-like structure of data. Experimental results on standard citation networks datasets with semi-supervised learning show that our approach yields new state-of-the-art results of accuracy 74.7$\%$ on Citeseer and 81.3$\%$ on PubMed datasets. Furthermore, we observe that our approach can be trained up to two orders of magnitude faster than other nonlinear GCN models on PubMed dataset. Our code is publicly available at https://github.com/llqy123/LLGC-master., Comment: Accepted by LREC-COLING 2024

Published
2024

25. Morphological Knowledge Guided Mongolian Constituent Parsing

Author

Liu, Na, Su, Xiangdong, Gao, Guanglai, Bao, Feilong, Lu, Min, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gedeon, Tom, editor, Wong, Kok Wai, editor, and Lee, Minho, editor

Published
2019
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26. A Natural Scene Text Extraction Approach Based on Generative Adversarial Learning

Author

Xu, Huali, Su, Xiangdong, Liu, Tongyang, Guo, Pengcheng, Gao, Guanglai, Bao, Feilong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gedeon, Tom, editor, Wong, Kok Wai, editor, and Lee, Minho, editor

Published
2019
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27. An Attention-Based Approach for Mongolian News Named Entity Recognition

Author

Tan, Mingyan, Bao, Feilong, Gao, Guanglai, Wang, Weihua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sun, Maosong, editor, Huang, Xuanjing, editor, Ji, Heng, editor, Liu, Zhiyuan, editor, and Liu, Yang, editor

Published
2019
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28. A Context-Free Spelling Correction Method for Classical Mongolian

Author

Lu, Min, Bao, Feilong, Gao, Guanglai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tang, Jie, editor, Kan, Min-Yen, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2019
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29. End-to-End Model for Offline Handwritten Mongolian Word Recognition

Author

Wei, Hongxi, Liu, Cong, Zhang, Hui, Bao, Feilong, Gao, Guanglai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tang, Jie, editor, Kan, Min-Yen, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2019
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30. Research on Khalkha Dialect Mongolian Speech Recognition Acoustic Model Based on Weight Transfer

Author

Shi, Linyan, Bao, Feilong, Wang, Yonghe, Gao, Guanglai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tang, Jie, editor, Kan, Min-Yen, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2019
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31. An Automatic Spelling Correction Method for Classical Mongolian

Author

Lu, Min, Bao, Feilong, Gao, Guanglai, Wang, Weihua, Zhang, Hui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Douligeris, Christos, editor, Karagiannis, Dimitris, editor, and Apostolou, Dimitris, editor

Published
2019
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33. Mongolian Text-to-Speech System Based on Deep Neural Network

Author

Liu, Rui, Bao, Feilong, Gao, Guanglai, Wang, Yonghe, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Tao, Jianhua, editor, Zheng, Thomas Fang, editor, Bao, Changchun, editor, Wang, Dong, editor, and Li, Ya, editor

Published
2018
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34. Mongolian Grapheme to Phoneme Conversion by Using Hybrid Approach

Author

Liu, Zhinan, Bao, Feilong, Gao, Guanglai, Suburi, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Zhang, Min, editor, Ng, Vincent, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2018
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35. Phonologically Aware BiLSTM Model for Mongolian Phrase Break Prediction with Attention Mechanism

Author

Liu, Rui, Bao, FeiLong, Gao, Guanglai, Zhang, Hui, Wang, Yonghe, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Geng, Xin, editor, and Kang, Byeong-Ho, editor

Published
2018
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36. Research on Mongolian Speech Recognition Based on FSMN

Author

Wang, Yonghe, Bao, Feilong, Zhang, Hongwei, Gao, Guanglai, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Xuanjing, editor, Jiang, Jing, editor, Zhao, Dongyan, editor, Feng, Yansong, editor, and Hong, Yu, editor

Published
2018
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37. Mongolian Word Segmentation Based on Three Character Level Seq2Seq Models

Author

Liu, Na, Su, Xiangdong, Gao, Guanglai, Bao, Feilong, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Cheng, Long, editor, Leung, Andrew Chi Sing, editor, and Ozawa, Seiichi, editor

Published
2018
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45. Language Model for Mongolian Polyphone Proofreading

Author

Lu, Min, Bao, Feilong, Gao, Guanglai, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sun, Maosong, editor, Wang, Xiaojie, editor, Chang, Baobao, editor, and Xiong, Deyi, editor

Published
2017
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48. Cyrillic Mongolian Named Entity Recognition with Rich Features

Author

Wang, Weihua, Bao, Feilong, Gao, Guanglai, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lin, Chin-Yew, editor, Xue, Nianwen, editor, Zhao, Dongyan, editor, Huang, Xuanjing, editor, and Feng, Yansong, editor

Published
2016
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49. A Novel Approach to Improve the Mongolian Language Model Using Intermediate Characters

Author

Yan, Xiaofei, Bao, Feilong, Wei, Hongxi, Su, Xiangdong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sun, Maosong, editor, Huang, Xuanjing, editor, Lin, Hongfei, editor, Liu, Zhiyuan, editor, and Liu, Yang, editor

Published
2016
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