semantic processing is a fundamental research domain in computational
linguistics. In the era of powerful pre-trained language models and large
language models, the advancement of research in this domain appears to be
decelerating. However, the study of semantics is multi-dimensional i
本文述及基于最近三年的神经网络技术,通过直接从语音信号中提取语义,取代传统的分类式自然语言处理方式,以 spoken language understanding 话题研究为主,在利用未标记的数据进行自我监督训练方面取得了新的突破。同时介绍了针对法语 MEDIA 数据集的最新进展,并提出了得到明显改进的成果,概念误差率(CER)从现有最优系统的 13.6% 降至 11.2%。