Cited by 449Natural Language Processing TechniquesDomain Adaptation and Few-Shot LearningTopic Modeling

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Deep multi-task learning with low level tasks supervised at lower layers

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Søgaard, Anders, Goldberg, Yoav·2016·Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

DOI 10.18653/v1/p16-2038

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Abstract

In all previous work on deep multi-task learning we are aware of, all task supervisions are on the same (outermost) layer. We present a multi-task learning architecture with deep bi-directional RNNs, where different tasks supervision can happen at different layers. We present experiments in syntactic chunking and CCG supertagging, coupled with the additional task of POS-tagging. We show that it is consistently better to have POS supervision at the innermost rather than the outermost layer. We argue that this is because “lowlevel” tasks are better kept at the lower layers, enabling the higher-level tasks to make use of the shared representation of the lower-level tasks. Finally, we also show how this architecture can be used for domain adaptation.

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Authors: Søgaard, A., & Goldberg, Y.
Affiliations: University of Copenhagen (DK)
ORCID: Søgaard, Anders
Goldberg, Yoav
Venue: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Publisher: Association for Computational Linguistics
Published: 2016
Pages: 231-235
ISSN: 0736-587X (ISSN-L)
DOI: 10.18653/v1/p16-2038
OpenAlex: W2516255829
Cited by: 445
References: 16
Citations by year: 2026: 5 · 2025: 11 · 2024: 12 · 2023: 32 · 2022: 35 · 2021: 42 · 2020: 87 · 2019: 95 · 2018: 86 · 2017: 37 · 2016: 2
FWCI: 39.51
Type: conference paper
Language: en
Open access: bronze
doi.org
Locations: Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion)
Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: multi-task deep learning, bidirectional RNN, part-of-speech tagging, shared representations, domain adaptation

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Deep multi-task learning with low level tasks supervised at lower layers

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