Jointly Learning to Label Sentences and Tokens
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Authors
Rei, Marek
Sogaard, Anders
AAAI
Publication Date
2019Journal Title
THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
Conference Name
AAAI 2019
Pages
6916-6923
Type
Conference Object
This Version
AM
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Rei, M., Sogaard, A., & AAAI. (2019). Jointly Learning to Label Sentences and Tokens. THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 6916-6923. https://doi.org/10.17863/CAM.35477
Abstract
Learning to construct text representations in end-to-end systems can be difficult, as natural languages are highly compositional and task-specific annotated datasets are often limited in size.
Methods for directly supervising language composition can allow us to guide the models based on existing knowledge, regularizing them towards more robust and interpretable representations.
In this paper, we investigate how objectives at different granularities can be used to learn better language representations and we propose an architecture for jointly learning to label sentences and tokens.
The predictions at each level are combined together using an attention mechanism, with token-level labels also acting as explicit supervision for composing sentence-level representations.
Our experiments show that by learning to perform these tasks jointly on multiple levels, the model achieves substantial improvements for both sentence classification and sequence labeling.
Sponsorship
Cambridge Assessment (unknown)
Identifiers
External DOI: https://doi.org/10.17863/CAM.35477
This record's URL: https://www.repository.cam.ac.uk/handle/1810/288161
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Licence:
http://www.rioxx.net/licenses/all-rights-reserved
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