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        <title>Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies</title>
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        <namePart type="given">Shuhei</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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    <abstract>In Semantic Dependency Parsing (SDP), semantic relations form directed acyclic graphs, rather than trees. We propose a new iterative predicate selection (IPS) algorithm for SDP. Our IPS algorithm combines the graph-based and transition-based parsing approaches in order to handle multiple semantic head words. We train the IPS model using a combination of multi-task learning and task-specific policy gradient training. Trained this way, IPS achieves a new state of the art on the SemEval 2015 Task 18 datasets. Furthermore, we observe that policy gradient training learns an easy-first strategy.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1232</identifier>
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%0 Conference Proceedings
%T Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
%A Kurita, Shuhei
%A Søgaard, Anders
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F kurita-sogaard-2019-multi
%X In Semantic Dependency Parsing (SDP), semantic relations form directed acyclic graphs, rather than trees. We propose a new iterative predicate selection (IPS) algorithm for SDP. Our IPS algorithm combines the graph-based and transition-based parsing approaches in order to handle multiple semantic head words. We train the IPS model using a combination of multi-task learning and task-specific policy gradient training. Trained this way, IPS achieves a new state of the art on the SemEval 2015 Task 18 datasets. Furthermore, we observe that policy gradient training learns an easy-first strategy.
%R 10.18653/v1/P19-1232
%U https://aclanthology.org/P19-1232
%U https://doi.org/10.18653/v1/P19-1232
%P 2420-2430

Markdown (Informal)

[Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies](https://aclanthology.org/P19-1232) (Kurita & Søgaard, ACL 2019)

• Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies (Kurita & Søgaard, ACL 2019)

ACL

• Shuhei Kurita and Anders Søgaard. 2019. Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2420–2430, Florence, Italy. Association for Computational Linguistics.