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        <title>SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations</title>
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    <abstract>We describe the SEx BiST parser (Semantically EXtended Bi-LSTM parser) developed at Lattice for the CoNLL 2018 Shared Task (Multilingual Parsing from Raw Text to Universal Dependencies). The main characteristic of our work is the encoding of three different modes of contextual information for parsing: (i) Treebank feature representations, (ii) Multilingual word representations, (iii) ELMo representations obtained via unsupervised learning from external resources. Our parser performed well in the official end-to-end evaluation (73.02 LAS – 4th/26 teams, and 78.72 UAS – 2nd/26); remarkably, we achieved the best UAS scores on all the English corpora by applying the three suggested feature representations. Finally, we were also ranked 1st at the optional event extraction task, part of the 2018 Extrinsic Parser Evaluation campaign.</abstract>
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%0 Conference Proceedings
%T SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations
%A Lim, KyungTae
%A Park, Cheoneum
%A Lee, Changki
%A Poibeau, Thierry
%Y Zeman, Daniel
%Y Hajič, Jan
%S Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F lim-etal-2018-sex
%X We describe the SEx BiST parser (Semantically EXtended Bi-LSTM parser) developed at Lattice for the CoNLL 2018 Shared Task (Multilingual Parsing from Raw Text to Universal Dependencies). The main characteristic of our work is the encoding of three different modes of contextual information for parsing: (i) Treebank feature representations, (ii) Multilingual word representations, (iii) ELMo representations obtained via unsupervised learning from external resources. Our parser performed well in the official end-to-end evaluation (73.02 LAS – 4th/26 teams, and 78.72 UAS – 2nd/26); remarkably, we achieved the best UAS scores on all the English corpora by applying the three suggested feature representations. Finally, we were also ranked 1st at the optional event extraction task, part of the 2018 Extrinsic Parser Evaluation campaign.
%R 10.18653/v1/K18-2014
%U https://aclanthology.org/K18-2014
%U https://doi.org/10.18653/v1/K18-2014
%P 143-152

Markdown (Informal)

[SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations](https://aclanthology.org/K18-2014) (Lim et al., CoNLL 2018)

• SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations (Lim et al., CoNLL 2018)

ACL

• KyungTae Lim, Cheoneum Park, Changki Lee, and Thierry Poibeau. 2018. SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 143–152, Brussels, Belgium. Association for Computational Linguistics.