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Titlebook: Chinese Computational Linguistics; 21st China National Maosong Sun,Yang Liu,Yubo Chen Conference proceedings 2022 The Editor(s) (if applic

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發(fā)表于 2025-3-21 18:39:51 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Chinese Computational Linguistics
副標(biāo)題21st China National
編輯Maosong Sun,Yang Liu,Yubo Chen
視頻videohttp://file.papertrans.cn/226/225762/225762.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Chinese Computational Linguistics; 21st China National  Maosong Sun,Yang Liu,Yubo Chen Conference proceedings 2022 The Editor(s) (if applic
描述This book constitutes the proceedings of the 21st China National Conference on Computational Linguistics, CCL 2022, held in Nanchang, China, in October 2022..The 22 full English-language papers in this volume were carefully reviewed and selected from?293 Chinese and English submissions..The conference papers are categorized into the following topical sub-headings:?Linguistics and Cognitive Science;?Fundamental Theory and Methods of Computational Linguistics;?Information Retrieval, Dialogue and Question Answering;?Text Generation and Summarization;?Knowledge Graph and Information Extraction;?Machine Translation and Multilingual Information Processing;?Minority Language Information Processing;?Language Resource and Evaluation;?NLP Applications..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; computational linguistics; computer systems; computer vision; data mining; image
版次1
doihttps://doi.org/10.1007/978-3-031-18315-7
isbn_softcover978-3-031-18314-0
isbn_ebook978-3-031-18315-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Fundamental organization models,pairs. In the interaction layer, we initially fuse the information of the sentence pairs to obtain low-level semantic information; at the same time, we use the bi-directional attention in the machine reading comprehension model and self-attention to obtain the high-level semantic information. We use
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Markus F. Peschl,Thomas Fundneider emotion cause into the generation process. To this end, we present an emotion cause extractor using a semi-supervised training method and an empathetic conversation generator using a biased self-attention mechanism to overcome these two issues. Experimental results indicate that our proposed emotio
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Quantitative vs. Weighted Automata and design three patient strategies. Thirdly we design a label-aware contrastive learning loss function. Extensive experimental results show that our TempACL effectively adapts contrastive learning to supervised learning tasks which remain a challenge in practice. TempACL achieves new state-of-the-
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Discourse Markers as the Classificatory Factors of Speech Actsse markers . and . are rather efficacious in differentiating distinct speech acts. This paper indicates that quantitative indexes can reflect the characteristics of human speech acts, and more objective and data-based classification schemes might be achieved based on these metrics.
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