论文标题
多语言名称实体识别和意图分类采用深度学习体系结构
Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures
论文作者
论文摘要
指定的实体识别和意图分类是自然语言处理领域最重要的子场之一。最近的研究导致了更快，更复杂和高效的模型的发展，以解决这两个任务所带来的问题。在这项工作中，我们探讨了两个深度学习网络对这些任务的有效性：双向长期短期网络和基于变压器的网络。这些模型在ATIS基准数据集上接受了英语和希腊语言的培训和测试。本文的目的是对两种语言的两组网络进行比较研究，并展示我们实验的结果。这些模型是当前的最新技术，得出了令人印象深刻的结果并取得了高性能。
Named Entity Recognition and Intent Classification are among the most important subfields of the field of Natural Language Processing. Recent research has lead to the development of faster, more sophisticated and efficient models to tackle the problems posed by those two tasks. In this work we explore the effectiveness of two separate families of Deep Learning networks for those tasks: Bidirectional Long Short-Term networks and Transformer-based networks. The models were trained and tested on the ATIS benchmark dataset for both English and Greek languages. The purpose of this paper is to present a comparative study of the two groups of networks for both languages and showcase the results of our experiments. The models, being the current state-of-the-art, yielded impressive results and achieved high performance.
