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CASIA OpenIR
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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：207/82  |  提交时间：2023/06/13
dialogue summarization  abstractive summarization  controllable text generation  natural language processing  
Dual-View Conditional Variational Auto-Encoder for Emotional Dialogue Generation 期刊论文
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2022, 卷号: 21, 期号: 3, 页码: 18
作者:  Li, Mei;  Zhang, Jiajun;  Lu, Xiang;  Zong, Chengqing
Adobe PDF(1187Kb)  |  收藏  |  浏览/下载：289/59  |  提交时间：2022/06/10
Sentiment  dialogue  neural networks  
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：268/58  |  提交时间：2021/12/28
Cross-lingual  dialogue system  adversarial learning  knowledge  robustness  
Graph-based Multimodal Ranking Models for Multimodal Summarization 期刊论文
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2021, 卷号: 20, 期号: 4, 页码: 21
作者:  Zhu, Junnan;  Xiang, Lu;  Zhou, Yu;  Zhang, Jiajun;  Zong, Chengqing
Adobe PDF(4193Kb)  |  收藏  |  浏览/下载：280/52  |  提交时间：2021/12/28
Multimodal summarization  single-modal  multimodal ranking  unsupervised  
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：268/57  |  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