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        <title>Semi-supervised Multitask Learning for Sequence Labeling</title>
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        <namePart type="given">Marek</namePart>
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    <abstract>We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks. The architecture was evaluated on a range of datasets, covering the tasks of error detection in learner texts, named entity recognition, chunking and POS-tagging. The novel language modeling objective provided consistent performance improvements on every benchmark, without requiring any additional annotated or unannotated data.</abstract>
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    <identifier type="doi">10.18653/v1/P17-1194</identifier>
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%0 Conference Proceedings
%T Semi-supervised Multitask Learning for Sequence Labeling
%A Rei, Marek
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F rei-2017-semi
%X We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks. The architecture was evaluated on a range of datasets, covering the tasks of error detection in learner texts, named entity recognition, chunking and POS-tagging. The novel language modeling objective provided consistent performance improvements on every benchmark, without requiring any additional annotated or unannotated data.
%R 10.18653/v1/P17-1194
%U https://aclanthology.org/P17-1194
%U https://doi.org/10.18653/v1/P17-1194
%P 2121-2130

Markdown (Informal)

[Semi-supervised Multitask Learning for Sequence Labeling](https://aclanthology.org/P17-1194) (Rei, ACL 2017)

• Semi-supervised Multitask Learning for Sequence Labeling (Rei, ACL 2017)

ACL

• Marek Rei. 2017. Semi-supervised Multitask Learning for Sequence Labeling. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2121–2130, Vancouver, Canada. Association for Computational Linguistics.