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        <namePart type="given">Tobias</namePart>
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            <title>Proceedings of the 13th International Workshop on Semantic Evaluation</title>
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            <namePart type="given">Jonathan</namePart>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>Our submission for Task 1 ‘Cross-lingual Semantic Parsing with UCCA’ at SemEval-2018 is a feed-forward neural network that builds upon an existing state-of-the-art transition-based directed acyclic graph parser. We replace most of its features by deep contextualized word embeddings and introduce an approximation to represent non-terminal nodes in the graph as an aggregation of their terminal children. We further demonstrate how augmenting data using the baseline systems provides a consistent advantage in all open submission tracks. We submitted results to all open tracks (English, in- and out-of-domain, German in-domain and French in-domain, low-resource). Our system achieves competitive performance in all settings besides the French, where we did not augment the data. Post-evaluation experiments showed that data augmentation is especially crucial in this setting.</abstract>
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%0 Conference Proceedings
%T Tüpa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing
%A Pütz, Tobias
%A Glocker, Kevin
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F putz-glocker-2019-tupa
%X Our submission for Task 1 ‘Cross-lingual Semantic Parsing with UCCA’ at SemEval-2018 is a feed-forward neural network that builds upon an existing state-of-the-art transition-based directed acyclic graph parser. We replace most of its features by deep contextualized word embeddings and introduce an approximation to represent non-terminal nodes in the graph as an aggregation of their terminal children. We further demonstrate how augmenting data using the baseline systems provides a consistent advantage in all open submission tracks. We submitted results to all open tracks (English, in- and out-of-domain, German in-domain and French in-domain, low-resource). Our system achieves competitive performance in all settings besides the French, where we did not augment the data. Post-evaluation experiments showed that data augmentation is especially crucial in this setting.
%R 10.18653/v1/S19-2016
%U https://aclanthology.org/S19-2016
%U https://doi.org/10.18653/v1/S19-2016
%P 113-118

Markdown (Informal)

[Tüpa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing](https://aclanthology.org/S19-2016) (Pütz & Glocker, SemEval 2019)

• Tüpa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing (Pütz & Glocker, SemEval 2019)

ACL

• Tobias Pütz and Kevin Glocker. 2019. Tüpa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 113–118, Minneapolis, Minnesota, USA. Association for Computational Linguistics.