Abstrakt
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 |
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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 |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP'. Sammen danner de et unikt fingeraftryk.Priser
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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