Meta Learning for Natural Language Processing: A Survey

Anonymous

Paper Link: https://openreview.net/forum?id=5FENjGCL0Eu

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: Deep learning has been the mainstream technique in the natural language processing (NLP) area. However, deep learning requires many labeled data and is less generalizable across domains. Meta-learning is an arising field in machine learning. It studies approaches to learning better learning algorithms and aims to improve algorithms in various aspects, including data efficiency and generalizability. The efficacy of meta-learning has been shown in many NLP tasks, but there is no systematic survey of these approaches in NLP, which hinders more researchers from joining the field. Our goal with this survey paper is to offer researchers pointers to relevant meta-learning works in NLP and attract more attention from the NLP community to drive future innovation. This paper first introduces the general concepts of meta-learning and the common approaches. Then we summarize task construction settings, applications of meta-learning for various NLP problems and review the development of meta-learning in the NLP community.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Hung-yi Lee

Copyright Consent Name And Address: National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617 Taiwan