Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP
IT University of Copenhagen（哥本哈根信息技术大学）；University of Groningen（格罗宁根大学）；University of Trento（特伦托大学）；Factmata（Factmata公司）；Microsoft Research - University of Trento（微软研究院-特伦托大学）
英文摘要：
Transfer learning, particularly approaches that combine multi-task learning with pre-trained contextualized embeddings and fine-tuning, have advanced the field of Natural Language Processing tremendously in recent years. In this paper we present MaChAmp, a toolkit for easy fine-tuning of contextualized embeddings in multi-task settings. The benefits of MaChAmp are its flexible configuration options, and the support of a variety of natural language processing tasks in a uniform toolkit, from text classification and sequence labeling to dependency parsing, masked language modeling, and text generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
