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Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP

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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.

  • Tags:
    • MTL
    • Multi-task learning
    • sequence labeling
    • dependency parsing
    • text generation
    • masked language modeling
  • AllenNLP Version: 1.3
  • Languages: Unknown
  • Datasets:
  • Submitted On Mar 30, 2021