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        <title>A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks</title>
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        <namePart type="given">Jiang</namePart>
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    <name type="personal">
        <namePart type="given">Haifeng</namePart>
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    <name type="personal">
        <namePart type="given">Ting</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>Various treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain common syntactic knowledge that is potential to benefit each other. This paper presents a universal framework for transfer parsing across multi-typed treebanks with deep multi-task learning. We consider two kinds of treebanks as source: the multilingual universal treebanks and the monolingual heterogeneous treebanks. Knowledge across the source and target treebanks are effectively transferred through multi-level parameter sharing. Experiments on several benchmark datasets in various languages demonstrate that our approach can make effective use of arbitrary source treebanks to improve target parsing models.</abstract>
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%0 Conference Proceedings
%T A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks
%A Guo, Jiang
%A Che, Wanxiang
%A Wang, Haifeng
%A Liu, Ting
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F guo-etal-2016-universal
%X Various treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain common syntactic knowledge that is potential to benefit each other. This paper presents a universal framework for transfer parsing across multi-typed treebanks with deep multi-task learning. We consider two kinds of treebanks as source: the multilingual universal treebanks and the monolingual heterogeneous treebanks. Knowledge across the source and target treebanks are effectively transferred through multi-level parameter sharing. Experiments on several benchmark datasets in various languages demonstrate that our approach can make effective use of arbitrary source treebanks to improve target parsing models.
%U https://aclanthology.org/C16-1002
%P 12-22

Markdown (Informal)

[A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks](https://aclanthology.org/C16-1002) (Guo et al., COLING 2016)

• A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks (Guo et al., COLING 2016)

ACL

• Jiang Guo, Wanxiang Che, Haifeng Wang, and Ting Liu. 2016. A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 12–22, Osaka, Japan. The COLING 2016 Organizing Committee.