@inproceedings{van-der-goot-etal-2021-massive,
title = "Massive Choice, Ample Tasks ({M}a{C}h{A}mp): A Toolkit for Multi-task Learning in {NLP}",
author = {van der Goot, Rob and
{\"U}st{\"u}n, Ahmet and
Ramponi, Alan and
Sharaf, Ibrahim and
Plank, Barbara},
editor = "Gkatzia, Dimitra and
Seddah, Djam{\'e}",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.22",
doi = "10.18653/v1/2021.eacl-demos.22",
pages = "176--197",
abstract = "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.",
}
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%0 Conference Proceedings
%T Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP
%A van der Goot, Rob
%A Üstün, Ahmet
%A Ramponi, Alan
%A Sharaf, Ibrahim
%A Plank, Barbara
%Y Gkatzia, Dimitra
%Y Seddah, Djamé
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F van-der-goot-etal-2021-massive
%X 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.
%R 10.18653/v1/2021.eacl-demos.22
%U https://aclanthology.org/2021.eacl-demos.22
%U https://doi.org/10.18653/v1/2021.eacl-demos.22
%P 176-197
Markdown (Informal)
[Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP](https://aclanthology.org/2021.eacl-demos.22) (van der Goot et al., EACL 2021)
ACL