郑州大学学报（工学版）2024，Vol.45Issue(1)：78-89,12.DOI:10.13705/j.issn.1671-6833.2024.01.012
基于小样本学习的口语理解方法综述
A Survey of Spoken Language Understanding Based on Few-shot Learning
摘要
Abstract
Few-shot spoken language understanding(SLU)is one of the urgent problems in dialogue artificial intel-ligence(DAI).The relevant literature on SLU task,combining the latest research trends both domestic and foreign was systematically reviewed.The classic methods for SLU task modeling in non-few-shot scenarios were briefly in-troduced,including single modeling,implicit joint modeling,explicit joint modeling,and pre-trained paradigms.The latest studies in few-shot SLU were introduced,which included three kinds of few-shot learning methods based on model fine-tuning,data augmentation and metric learning.Representative models such as ULMFiT,prototypical network,and induction network were discussed.On this basis,the semantic understanding ability,interpretability,generalization ability and other performances of different methods were analyzed and compared.Finally,the chal-lenges and future development directions of SLU tasks were discussed,it was pointed out that zero-shot SLU,Chi-nese SLU,open-domain SLU,and cross-lingual SLU would be the research difficulties in this field.关键词
口语理解/小样本学习/模型微调/数据增强/度量学习Key words
spoken language understanding/few-shot learning/fine-tune/data augmentation/metric learning分类
信息技术与安全科学引用本文复制引用
刘纳,郑国风,徐贞顺,林令德,李晨,杨杰..基于小样本学习的口语理解方法综述[J].郑州大学学报（工学版）,2024,45(1):78-89,12.基金项目
宁夏自然科学基金资助项目(2021AAC03224,2021AAC03217) （2021AAC03224,2021AAC03217）
