Author: Zen and the Art of Computer Programming
1 Introduction
"Meta learning" is a machine learning method that enables the model to learn the knowledge structure, knowledge representation and knowledge extraction capabilities during training, so that the model can be applied to new data and improve its generalization performance. Nowadays, "meta-learning" has become a hot direction in the field of natural language processing. In this article, we will elaborate on "meta-learning" from three perspectives: lexical level, syntactic level and knowledge level. At the same time, we will also discuss the current research progress and key challenges of "meta-learning" based on actual cases, and provide corresponding solutions.
1. Background introduction
In recent years, deep learning has exploded. In order to break through the limitations of traditional machine learning techniques, using deep neural networks to train models has become one of the most popular methods today. But at the same time, deep learning also has some problems. On the one hand, it requires a large amount of data for training; on the other hand, because it is trained in an end-to-end manner, the feature space learned by the model is relatively general, and it is often unable to adapt to complex natural language processing tasks. How to improve the effectiveness and effectiveness of models in natural language processing has been a concern of researchers in recent years. Therefore, the "meta-learning" method came into being.
2. Explanation of basic concepts and terms
"Meta-learning" can be thought of as a comprehensive approach. Its basic idea is to predict, generate or infer the pre-training parameters of another model by learning one model. Generally speaking, meta-learning models are mainly divided into two categories: deep learning-based meta-learning models and non-deep learning-based meta-learning models.
2.1 Meta-learning model for deep learning
model definition
The Deep Meta Learning Model (DMLM) refers to the meta-learning model based on deep learning, which mainly includes the following components: