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

Rob van der Goot; Ahmet Üstün; Alan Ramponi; Ibrahim Sharaf; Barbara Plank

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

Rob van der Goot, Ahmet Üstün, Alan Ramponi, Ibrahim Sharaf, Barbara Plank

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Abstract

Transfer learning, particularly approaches that combine multi-task learning with pretrained 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.

Original language	English
Title of host publication	Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
Publisher	Association for Computational Linguistics
Publication date	2021
Pages	176-197
Publication status	Published - 2021

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2021.eacl-demos.22Accepted author manuscript, 559 KB

https://www.aclweb.org/anthology/2021.eacl-demos.22/

Outstanding paper award, EACL 2021 demo track
van der Goot, Rob (Recipient), Üstün, Ahmet (Recipient), Ramponi, Alan (Recipient), Sharaf, Ibrahim (Recipient) & Plank, Barbara (Recipient), 2021
Prize: Prizes, scholarships, distinctions

Cite this

van der Goot, R., Üstün, A., Ramponi, A., Sharaf, I., & Plank, B. (2021). Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (pp. 176-197). Association for Computational Linguistics. https://www.aclweb.org/anthology/2021.eacl-demos.22/

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

abstract = "Transfer learning, particularly approaches that combine multi-task learning with pretrained 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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year = "2021",

language = "English",

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booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",

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van der Goot, R, Üstün, A, Ramponi, A, Sharaf, I & Plank, B 2021, Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP. in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics, pp. 176-197. <https://www.aclweb.org/anthology/2021.eacl-demos.22/>

Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP. / van der Goot, Rob; Üstün, Ahmet; Ramponi, Alan et al.

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics, 2021. p. 176-197.

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

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T1 - Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP

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AU - Sharaf, Ibrahim

AU - Plank, Barbara

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N2 - Transfer learning, particularly approaches that combine multi-task learning with pretrained 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.

AB - Transfer learning, particularly approaches that combine multi-task learning with pretrained 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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M3 - Article in proceedings

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EP - 197

BT - Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

PB - Association for Computational Linguistics

ER -

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

Abstract

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Prizes

Outstanding paper award, EACL 2021 demo track

Cite this