Zobrazeno 1 - 7
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pro vyhledávání: '"Chang, Yongzhu"'
Autor:
Pu, Jiashu, Wan, Yajing, Zhang, Yuru, Chen, Jing, Cheng, Ling, Shao, Qian, Chang, Yongzhu, Lv, Tangjie, Zhang, Rongsheng
Previous in-context learning (ICL) research has focused on tasks such as classification, machine translation, text2table, etc., while studies on whether ICL can improve human-like dialogue generation are scarce. Our work fills this gap by systematica
Externí odkaz:
http://arxiv.org/abs/2402.09954
Autor:
Pu, Jiashu, Zhao, Shiwei, Cheng, Ling, Chang, Yongzhu, Wu, Runze, Lv, Tangjie, Zhang, Rongsheng
Although the pre-training followed by fine-tuning paradigm is used extensively in many fields, there is still some controversy surrounding the impact of pre-training on the fine-tuning process. Currently, experimental findings based on text and image
Externí odkaz:
http://arxiv.org/abs/2309.05256
Publikováno v:
publish EMNLP 2023
Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control such as keywords, rhymes, etc. However, lyrics imitation, which involves
Externí odkaz:
http://arxiv.org/abs/2308.04665
Publikováno v:
published ICDAR 2023 D-NLP
Simile detection is a valuable task for many natural language processing (NLP)-based applications, particularly in the field of literature. However, existing research on simile detection often relies on corpora that are limited in size and do not ade
Externí odkaz:
http://arxiv.org/abs/2308.04109
Autor:
Chen, Weijie, Chang, Yongzhu, Zhang, Rongsheng, Pu, Jiashu, Chen, Guandan, Zhang, Le, Xi, Yadong, Chen, Yijiang, Su, Chang
Simile interpretation (SI) and simile generation (SG) are challenging tasks for NLP because models require adequate world knowledge to produce predictions. Previous works have employed many hand-crafted resources to bring knowledge-related into model
Externí odkaz:
http://arxiv.org/abs/2204.12807
Publikováno v:
In Neurocomputing 1 January 2025 611
Existing task-oriented chatbots heavily rely on spoken language understanding (SLU) systems to determine a user's utterance's intent and other key information for fulfilling specific tasks. In real-life applications, it is crucial to occasionally ind
Externí odkaz:
http://arxiv.org/abs/2201.06731