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

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › 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.

Originalsprog	Engelsk
Titel	Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
Forlag	Association for Computational Linguistics
Publikationsdato	2021
Sider	176-197
Status	Udgivet - 2021

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2021.eacl-demos.22Accepteret manuskript, 559 KB

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

Outstanding paper award, EACL 2021 demo track
van der Goot, Rob (Modtager), Üstün, Ahmet (Modtager), Ramponi, Alan (Modtager), Sharaf, Ibrahim (Modtager) & Plank, Barbara (Modtager), 2021
Pris: Priser, stipendier, udnævnelser

Citationsformater

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. I Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (s. 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.",

author = "{van der Goot}, Rob and Ahmet {\"U}st{\"u}n and Alan Ramponi and Ibrahim Sharaf and Barbara Plank",

year = "2021",

language = "English",

pages = "176--197",

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

publisher = "Association for Computational Linguistics",

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

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › 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

PY - 2021

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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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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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Fingeraftryk

Priser

Outstanding paper award, EACL 2021 demo track

Citationsformater