Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies. / Kurita, Shuhei; Søgaard, Anders.

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2019. p. 2420-2430.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Kurita, S & Søgaard, A 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. Association for Computational Linguistics, pp. 2420-2430, 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 01/07/2019. https://doi.org/10.18653/v1/P19-1232

APA

Kurita, S., & Søgaard, A. (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 (pp. 2420-2430). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1232

Vancouver

Kurita S, Søgaard A. 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. Association for Computational Linguistics. 2019. p. 2420-2430 https://doi.org/10.18653/v1/P19-1232

Author

Kurita, Shuhei ; Søgaard, Anders. / Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2019. pp. 2420-2430

Bibtex

@inproceedings{4fdb0f500f3f4ad1a82a97b9d154ad20,
title = "Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies",
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.",
author = "Shuhei Kurita and Anders S{\o}gaard",
year = "2019",
doi = "10.18653/v1/P19-1232",
language = "English",
pages = "2420--2430",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics",
note = "57th Annual Meeting of the Association for Computational Linguistics ; Conference date: 01-07-2019 Through 01-07-2019",

}

RIS

TY - GEN

T1 - Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies

AU - Kurita, Shuhei

AU - Søgaard, Anders

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

U2 - 10.18653/v1/P19-1232

DO - 10.18653/v1/P19-1232

M3 - Article in proceedings

SP - 2420

EP - 2430

BT - Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

PB - Association for Computational Linguistics

T2 - 57th Annual Meeting of the Association for Computational Linguistics

Y2 - 1 July 2019 through 1 July 2019

ER -

ID: 240408754