Feb. 27, 2024, 5:49 a.m. | Yingsi Yu, Cunliang Kong, Liner Yang, Meishan Zhang, Lin Zhu, Yujie Wang, Haozhe Lin, Maosong Sun, Erhong Yang

cs.CL updates on arXiv.org arxiv.org

arXiv:2402.16311v1 Announce Type: new
Abstract: Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching.Existing SPS parsers rely heavily on textbook corpora for training, lacking cross-domain capability.To overcome this constraint, this paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework. Partial syntactic rules from a source domain are combined with target domain sentences to dynamically generate training data, enhancing the adaptability of the parser to diverse domains.Experiments conducted on textbook …

abstract analysis arxiv capability chinese cs.ai cs.cl domain framework language language models large language large language models llms paper parsing rules self-training teaching textbook training type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

#13721 - Data Engineer - AI Model Testing

@ Qualitest | Miami, Florida, United States

Elasticsearch Administrator

@ ManTech | 201BF - Customer Site, Chantilly, VA