文献类型：期刊文献

英文题名：A Context-Enhanced Generate-then-Evaluate Framework for Chinese Abbreviation Prediction

作者：Tong, Hanwen[1]; Xie, Chenhao[2]; Liang, Jiaqing[3]; He, Qianyu[1]; Yue, Zhiang[1]; Liu, Jingping[4]; Xiao, Yanghua[1,5]; Wang, Wenguang[6]

机构：[1] Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China; [2] SenseDeal Intelligent Technology Co., Ltd., Beijing, China; [3] School of Data Science, Fudan University, Shanghai, China; [4] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; [5] Fudan-Aishu Cognitive Intelligence, Joint Research Center, Shanghai, China; [6] DataGrand Inc., Shanghai, China

年份：2022

起止页码：1945

外文期刊名：International Conference on Information and Knowledge Management, Proceedings

收录：EI(收录号：20224413038237)

语种：英文

外文关键词：Natural language processing systems

摘要：As a popular form of lexicalization, abbreviation is widely used in both oral and written language and plays an important role in various Natural Language Processing applications. However, current approaches cannot ensure that the predicted abbreviation preserves the meaning of its full form and maintains fluency. In this paper, we introduce a fresh perspective to evaluate the quality of abbreviations within their textual contexts with pre-trained language model. To this end, we propose a novel two-stage generate-then-evaluate framework enhanced by context, which consists of a generation model to generate multiple candidate abbreviations and an evaluation model to evaluate their quality within their contexts. Experimental results show that our framework consistently outperforms all the existing approaches, achieving 53.2% Hit@1 performance with a 5.6 points improvement compared to its previous best result. Our code and data are publicly available at https://github.com/HavenTong/CEGE. ? 2022 ACM.