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Titlebook: Chinese Computational Linguistics; 22nd China National Maosong Sun,Bing Qin,Yubo Chen Conference proceedings 2023 The Editor(s) (if applic

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發(fā)表于 2025-3-21 19:50:22 | 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22nd China National
編輯Maosong Sun,Bing Qin,Yubo Chen
視頻videohttp://file.papertrans.cn/226/225765/225765.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Chinese Computational Linguistics; 22nd China National  Maosong Sun,Bing Qin,Yubo Chen Conference proceedings 2023 The Editor(s) (if applic
描述This book constitutes the refereed proceedings of the 22nd China National Conference on Chinese Computational Linguistics, CCL 2023, Harbin, China, August 3–5, 2023..?.The 82 full papers included in this book were carefully reviewed and selected from 278 submissions. They were organized in topical sections as follows: ?Fundamental Theory and Methods of Computational Linguistics, Information Retrieval, Dialogue and Question Answering, Text Generation, Dialogue and Summarization, Knowledge Graph and Information Extraction, Machine Translation and Multilingual Information Processing, Language Resource and Evaluation, Pre-trained Language Models, Social Computing and Sentiment Analysis, NLP Applications..?
出版日期Conference proceedings 2023
關(guān)鍵詞artificial intelligence; Informatics; computational linguistics; computer systems; computer vision; data
版次1
doihttps://doi.org/10.1007/978-981-99-6207-5
isbn_softcover978-981-99-6206-8
isbn_ebook978-981-99-6207-5Series 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 Singapor
The information of publication is updating

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Learning on?Structured Documents for?Conditional Question Answeringined with LSD on massive unlabeled structured documents and is fine-tuned on labeled CQA dataset afterwards. To overcome the limitation of outputting answers with complex formats in CQA, we propose a pipeline that enables the generation of multiple answers and conditions. Experimental results on the
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Lexical Complexity Controlled Sentence Generation for?Language Learningodels. To facilitate the research, we develop two datasets in English and Chinese respectively, on which extensive experiments are conducted. Experimental results show that our approach provides more precise control over lexical complexity, as well as better fluency and diversity.
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Improving Zero-Shot Cross-Lingual Dialogue State Tracking via?Contrastive Learnings into a more refined language-invariant space. In addition, CLCL-DST uses a significance-based keyword extraction approach to select task-related words to build the bilingual dictionary for better cross-lingual positive examples. Experiment results on Multilingual WoZ 2.0 and parallel MultiWoZ 2.1
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