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        <title>Deep Contextualized Self-training for Low Resource Dependency Parsing</title>
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        <namePart type="given">Guy</namePart>
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    <abstract>Neural dependency parsing has proven very effective, achieving state-of-the-art results on numerous domains and languages. Unfortunately, it requires large amounts of labeled data, which is costly and laborious to create. In this paper we propose a self-training algorithm that alleviates this annotation bottleneck by training a parser on its own output. Our Deep Contextualized Self-training (DCST) algorithm utilizes representation models trained on sequence labeling tasks that are derived from the parser’s output when applied to unlabeled data, and integrates these models with the base parser through a gating mechanism. We conduct experiments across multiple languages, both in low resource in-domain and in cross-domain setups, and demonstrate that DCST substantially outperforms traditional self-training as well as recent semi-supervised training methods.1</abstract>
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    <identifier type="doi">10.1162/tacl_a_00294</identifier>
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        <date>2019</date>
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            <start>695</start>
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%0 Journal Article
%T Deep Contextualized Self-training for Low Resource Dependency Parsing
%A Rotman, Guy
%A Reichart, Roi
%J Transactions of the Association for Computational Linguistics
%D 2019
%V 7
%I MIT Press
%C Cambridge, MA
%F rotman-reichart-2019-deep
%X Neural dependency parsing has proven very effective, achieving state-of-the-art results on numerous domains and languages. Unfortunately, it requires large amounts of labeled data, which is costly and laborious to create. In this paper we propose a self-training algorithm that alleviates this annotation bottleneck by training a parser on its own output. Our Deep Contextualized Self-training (DCST) algorithm utilizes representation models trained on sequence labeling tasks that are derived from the parser’s output when applied to unlabeled data, and integrates these models with the base parser through a gating mechanism. We conduct experiments across multiple languages, both in low resource in-domain and in cross-domain setups, and demonstrate that DCST substantially outperforms traditional self-training as well as recent semi-supervised training methods.1
%9 journal article
%R 10.1162/tacl_a_00294
%U https://aclanthology.org/Q19-1044
%U https://doi.org/10.1162/tacl_a_00294
%P 695-713

Markdown (Informal)

[Deep Contextualized Self-training for Low Resource Dependency Parsing](https://aclanthology.org/Q19-1044) (Rotman & Reichart, TACL 2019)

• Deep Contextualized Self-training for Low Resource Dependency Parsing (Rotman & Reichart, TACL 2019)

ACL

• Guy Rotman and Roi Reichart. 2019. Deep Contextualized Self-training for Low Resource Dependency Parsing. Transactions of the Association for Computational Linguistics, 7:695–713.