Tsuruoka Yoshimasa
Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo.
Brain Nerve. 2019 Jan;71(1):45-55. doi: 10.11477/mf.1416201215.
The field of natural language processing (NLP) has seen rapid advances in the past several years since the introduction of deep learning techniques. A variety of NLP tasks including syntactic parsing, machine translation, and summarization can now be performed by relatively simple combinations of general neural network models such as recurrent neural networks and attention mechanisms. This manuscript gives a brief introduction to deep learning and an overview of the current deep learning-based NLP technology.
自深度学习技术引入以来，自然语言处理（NLP）领域在过去几年中取得了飞速进展。现在，包括句法分析、机器翻译和摘要生成在内的各种NLP任务都可以通过循环神经网络和注意力机制等通用神经网络模型的相对简单组合来执行。本文简要介绍了深度学习，并概述了当前基于深度学习的NLP技术。
