Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing

Abstract

Deep learning based natural language processing (NLP) has become the mainstream of research in recent years and significantly outperforms conventional methods. However, deep learning models are notorious for being data and computation hungry. These downsides limit such models' application from deployment to different domains, languages, countries, or styles, since collecting in-genre data and model training from scratch are costly. The long-tail nature of human language makes challenges even more significant.Meta-learning, or 'Learning to Learn', aims to learn better learning algorithms, including better parameter initialization, optimization strategy, network architecture, distance metrics, and beyond. Metalearning has been shown to allow faster fine-tuning, converge to better performance, and achieve outstanding results for few-shot learning in many applications. Meta-learning is one of the most important new techniques in machine learning in recent years, but the method is mainly investigated with applications in computer vision. It is believed that meta-learning has excellent potential to be applied in NLP, and some works have been proposed with notable achievements in several relevant problems, e.g., relation extraction, machine translation, and dialogue generation and state tracking. However, it does not catch the same level of attention as in the image processing community.