@inproceedings{wan-etal-2018-ibm,
title = "{IBM} Research at the {C}o{NLL} 2018 Shared Task on Multilingual Parsing",
author = "Wan, Hui and
Naseem, Tahira and
Lee, Young-Suk and
Castelli, Vittorio and
Ballesteros, Miguel",
editor = "Zeman, Daniel and
Haji{\v{c}}, Jan",
booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K18-2009",
doi = "10.18653/v1/K18-2009",
pages = "92--102",
abstract = "This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies. Our system implements a new joint transition-based parser, based on the Stack-LSTM framework and the Arc-Standard algorithm, that handles tokenization, part-of-speech tagging, morphological tagging and dependency parsing in one single model. By leveraging a combination of character-based modeling of words and recursive composition of partially built linguistic structures we qualified 13th overall and 7th in low resource. We also present a new sentence segmentation neural architecture based on Stack-LSTMs that was the 4th best overall.",
}
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<abstract>This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies. Our system implements a new joint transition-based parser, based on the Stack-LSTM framework and the Arc-Standard algorithm, that handles tokenization, part-of-speech tagging, morphological tagging and dependency parsing in one single model. By leveraging a combination of character-based modeling of words and recursive composition of partially built linguistic structures we qualified 13th overall and 7th in low resource. We also present a new sentence segmentation neural architecture based on Stack-LSTMs that was the 4th best overall.</abstract>
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%0 Conference Proceedings
%T IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing
%A Wan, Hui
%A Naseem, Tahira
%A Lee, Young-Suk
%A Castelli, Vittorio
%A Ballesteros, Miguel
%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 wan-etal-2018-ibm
%X This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies. Our system implements a new joint transition-based parser, based on the Stack-LSTM framework and the Arc-Standard algorithm, that handles tokenization, part-of-speech tagging, morphological tagging and dependency parsing in one single model. By leveraging a combination of character-based modeling of words and recursive composition of partially built linguistic structures we qualified 13th overall and 7th in low resource. We also present a new sentence segmentation neural architecture based on Stack-LSTMs that was the 4th best overall.
%R 10.18653/v1/K18-2009
%U https://aclanthology.org/K18-2009
%U https://doi.org/10.18653/v1/K18-2009
%P 92-102
Markdown (Informal)
[IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing](https://aclanthology.org/K18-2009) (Wan et al., CoNLL 2018)
ACL
- Hui Wan, Tahira Naseem, Young-Suk Lee, Vittorio Castelli, and Miguel Ballesteros. 2018. IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 92–102, Brussels, Belgium. Association for Computational Linguistics.