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CASIA OpenIR
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Born a BabyNet with Hierarchical Parental Supervision for End-to-End Text Image Machine Translation 会议论文
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italia, 20-25 May, 2024
作者:  Ma, Cong;  Zhang, Yaping;  Zhang, Zhiyang;  Liang, Yupu;  Zhao, Yang;  Zhou, Yu;  Zong, Chengqing
Adobe PDF(891Kb)  |  收藏  |  浏览/下载：24/10  |  提交时间：2024/06/27
Multi-teacher Knowledge Distillation for End-to-End Text Image Machine Translation 会议论文
Proceedings of the 17th Document Analysis and Recognition (ICDAR 2023), San José, California, USA, August 21-26, 2023
作者:  Ma, Cong;  Zhang, Yaping;  Tu, Mei;  Zhao, Yang;  Zhou, Yu;  Zong, Chengqing
Adobe PDF(1478Kb)  |  收藏  |  浏览/下载：31/13  |  提交时间：2024/06/26
Modal Contrastive Learning Based End-to-End Text Image Machine Translation 期刊论文
IEEE/ACM Transactions on Audio, Speech, and Language Processing (IEEE/ACM TASLP), 2023, 卷号: 32, 期号: 32, 页码: 2153-2165
作者:  Ma, Cong;  Han, Xu;  Wu, Linghui;  Zhang, Yaping;  Zhao, Yang;  Zhou, Yu;  Zong, Chengqing
Adobe PDF(6551Kb)  |  收藏  |  浏览/下载：31/16  |  提交时间：2024/06/26
Transformers  Machine translation  Decoding  Semantics  Pipelines  Text recognition  Task analysis  Text image machine translation  contrastive learning  text image recognition  machine translation  
Self-Modifying State Modeling for Simultaneous Machine Translation 会议论文
, Bangkok, Thailand, August 11–16, 2024
作者:  Donglei, Yu;  Xiaomian, Kang;  Yuchen, Liu;  YU, Zhou;  Chengqing, Zong
Adobe PDF(924Kb)  |  收藏  |  浏览/下载：26/13  |  提交时间：2024/06/20
Transformer: A General Framework from Machine Translation to Others 期刊论文
Machine Intelligence Research, 2023, 卷号: 20, 期号: 4, 页码: 514-538
作者:  Yang Zhao;  Jiajun Zhang;  Chengqing Zong
Adobe PDF(1415Kb)  |  收藏  |  浏览/下载：53/15  |  提交时间：2024/04/23
Neural machine translation, Transformer, document neural machine translation (NMT), multimodal NMT, low-resource NMT  
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization 会议论文
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, 2021-11-07 - 2021-11-11
作者:  Lin, Haitao;  Ma, Liqun;  Zhu, Junnan;  Xiang, Lu;  Zhou, Yu;  Zhang, Jiajun;  Zong, Chengqing
Adobe PDF(491Kb)  |  收藏  |  浏览/下载：155/44  |  提交时间：2023/06/13
Topic-Oriented Dialogue Summarization 期刊论文
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023, 卷号: 31, 页码: 1797 - 1810
作者:  Lin, Haitao;  Zhu, Junnan;  Xiang, Lu;  Zhai, Feifei;  Zhou, Yu;  Zhang, Jiajun;  Zong, Chengqing
Adobe PDF(3037Kb)  |  收藏  |  浏览/下载：245/90  |  提交时间：2023/06/13
dialogue summarization  abstractive summarization  controllable text generation  natural language processing  
Enhancing Lexical Translation Consistency for Document-Level Neural Machine Translation 期刊论文
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2022, 卷号: 21, 期号: 3, 页码: 21
作者:  Kang, Xiaomian;  Zhao, Yang;  Zhang, Jiajun;  Zong, Chengqing
Adobe PDF(2110Kb)  |  收藏  |  浏览/下载：301/21  |  提交时间：2022/06/10
Document-level translation  neural machine translation  lexical consistency  discourse phenomena  
Robust Cross-lingual Task-oriented Dialogue 期刊论文
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2021, 卷号: 20, 期号: 6, 页码: 24
作者:  Xiang, Lu;  Zhu, Junnan;  Zhao, Yang;  Zhou, Yu;  Zong, Chengqing
Adobe PDF(1935Kb)  |  收藏  |  浏览/下载：307/71  |  提交时间：2021/12/28
Cross-lingual  dialogue system  adversarial learning  knowledge  robustness  
Medical Term and Status Generation From Chinese Clinical Dialogue With Multi-Granularity Transformer 期刊论文
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2021, 卷号: 29, 页码: 3362-3374
作者:  Li, Mei;  Xiang, Lu;  Kang, Xiaomian;  Zhao, Yang;  Zhou, Yu;  Zong, Chengqing
Adobe PDF(3036Kb)  |  收藏  |  浏览/下载：318/72  |  提交时间：2021/12/28
Medical diagnostic imaging  Transformers  Task analysis  Medical services  Computational modeling  Semantics  Data mining  Medical dialogue  multi-granularity  attention mechanism  natural language understanding  sequence to sequence learning