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        <title>Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis</title>
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    <abstract>We present experiments for cross-domain semantic dependency analysis with a neural Maximum Subgraph parser. Our parser targets 1-endpoint-crossing, pagenumber-2 graphs which are a good fit to semantic dependency graphs, and utilizes an efficient dynamic programming algorithm for decoding. For disambiguation, the parser associates words with BiLSTM vectors and utilizes these vectors to assign scores to candidate dependencies. We conduct experiments on the data sets from SemEval 2015 as well as Chinese CCGBank. Our parser achieves very competitive results for both English and Chinese. To improve the parsing performance on cross-domain texts, we propose a data-oriented method to explore the linguistic generality encoded in English Resource Grammar, which is a precisionoriented, hand-crafted HPSG grammar, in an implicit way. Experiments demonstrate the effectiveness of our data-oriented method across a wide range of conditions.</abstract>
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%0 Conference Proceedings
%T Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis
%A Chen, Yufei
%A Huang, Sheng
%A Wang, Fang
%A Cao, Junjie
%A Sun, Weiwei
%A Wan, Xiaojun
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 oct
%I Association for Computational Linguistics
%C Brussels, Belgium
%F chen-etal-2018-neural
%X We present experiments for cross-domain semantic dependency analysis with a neural Maximum Subgraph parser. Our parser targets 1-endpoint-crossing, pagenumber-2 graphs which are a good fit to semantic dependency graphs, and utilizes an efficient dynamic programming algorithm for decoding. For disambiguation, the parser associates words with BiLSTM vectors and utilizes these vectors to assign scores to candidate dependencies. We conduct experiments on the data sets from SemEval 2015 as well as Chinese CCGBank. Our parser achieves very competitive results for both English and Chinese. To improve the parsing performance on cross-domain texts, we propose a data-oriented method to explore the linguistic generality encoded in English Resource Grammar, which is a precisionoriented, hand-crafted HPSG grammar, in an implicit way. Experiments demonstrate the effectiveness of our data-oriented method across a wide range of conditions.
%R 10.18653/v1/K18-1054
%U https://aclanthology.org/K18-1054
%U https://doi.org/10.18653/v1/K18-1054
%P 562-572

Markdown (Informal)

[Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis](https://aclanthology.org/K18-1054) (Chen et al., CoNLL 2018)

• Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis (Chen et al., CoNLL 2018)

ACL

• Yufei Chen, Sheng Huang, Fang Wang, Junjie Cao, Weiwei Sun, and Xiaojun Wan. 2018. Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 562–572, Brussels, Belgium. Association for Computational Linguistics.