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1
Multitask Pointer Network for Multi-Representational Parsing
Abstract: Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG ; [Abstract] Dependency and constituent trees are widely used by many artificial intelligence applications for representing the syntactic structure of human languages. Typically, these structures are separately produced by either dependency or constituent parsers. In this article, we propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees, supporting both continuous/projective and discontinuous/non-projective syntactic structures. To that end, we develop a Pointer Network architecture with two separate task-specific decoders and a common encoder, and follow a multitask learning strategy to jointly train them. The resulting quadratic system, not only becomes the first parser that can jointly produce both unrestricted constituent and dependency trees from a single model, but also proves that both syntactic formalisms can benefit from each other during training, achieving state-of-the-art accuracies in several widely-used benchmarks such as the continuous English and Chinese Penn Treebanks, as well as the discontinuous German NEGRA and TIGER datasets. ; We acknowledge the European Research Council (ERC), which has funded this research under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), ERDF/MICINN-AEI (ANSWER-ASAP, TIN2017-85160-C2-1-R; SCANNER-UDC, PID2020-113230RB-C21), Xunta de Galicia, Spain (ED431C 2020/11), and Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia, Spain and the European Union (ERDF - Galicia 2014–2020 Program), by grant ED431G 2019/01. Funding for open access charge: Universidade da Coruña / CISUG ; Xunta de Galicia; ED431C 2020/11 ; Xunta de Galicia; ED431G 2019/01
Keyword: Computational linguistics; Constituent parsing; Deep learning; Dependency parsing; Natural language processing; Neural network; Parsing
URL: https://doi.org/10.1016/j.knosys.2021.107760
http://hdl.handle.net/2183/29887
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2
Joint learning of morphology and syntax with cross-level contextual information flow
In: 2022 ; 1 ; 33 (2022)
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3
Analyse en dépendances du français avec des plongements contextualisés
In: 28e Conférence sur le Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-03223424 ; 28e Conférence sur le Traitement Automatique des Langues Naturelles, Jun 2021, Lille (virtuel), France (2021)
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4
IWPT 2021 Shared Task Data and System Outputs
Zeman, Daniel; Bouma, Gosse; Seddah, Djamé. - : Universal Dependencies Consortium, 2021
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5
Training corpus ssj500k 2.3
Krek, Simon; Dobrovoljc, Kaja; Erjavec, Tomaž. - : Centre for Language Resources and Technologies, University of Ljubljana, 2021
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6
PLPrepare: A Grammar Checker for Challenging Cases
In: Electronic Theses and Dissertations (2021)
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7
Treebank embedding vectors for out-of-domain dependency parsing
In: Wagner, Joachim orcid:0000-0002-8290-3849 , Barry, James orcid:0000-0003-3051-585X and Foster, Jennifer orcid:0000-0002-7789-4853 (2020) Treebank embedding vectors for out-of-domain dependency parsing. In: 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 05-10 Jul 2020, Online (virtual conference). (2020)
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8
Extrinsic Evaluation of French Dependency Parsers on a Specialized Corpus: Comparison of Distributional Thesauri
In: 12th Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02611042 ; 12th Language Resources and Evaluation Conference, May 2020, Marseille, France. pp.5822-5830 (2020)
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9
IWPT 2020 Shared Task Data and System Outputs
Zeman, Daniel; Bouma, Gosse; Seddah, Djamé. - : Universal Dependencies Consortium, 2020
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10
On understanding character-level models for representing morphology ...
Vania, Clara. - : The University of Edinburgh, 2020
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11
Self attended stack pointer networks for learning long term dependencies
Can, Burcu; Tuç, Salih. - : Association for Computational Linguistics, 2020
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12
Cross-lingual parsing with polyglot training and multi-treebank learning: a Faroese case study
In: Barry, James orcid:0000-0003-3051-585X , Wagner, Joachim orcid:0000-0002-8290-3849 and Foster, Jennifer orcid:0000-0002-7789-4853 (2019) Cross-lingual parsing with polyglot training and multi-treebank learning: a Faroese case study. In: The 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), 3 - 5 Nov 2019, Hong Kong, China. ISBN 978-1-950737-78-9 (2019)
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13
Recovering Chinese Nonlocal Dependencies with a Generalized Categorial Grammar
In: http://rave.ohiolink.edu/etdc/view?acc_num=osu154622673336324 (2019)
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14
Training corpus ssj500k 2.2
Krek, Simon; Dobrovoljc, Kaja; Erjavec, Tomaž. - : Centre for Language Resources and Technologies, University of Ljubljana, 2019
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15
GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks
Može, Sara; Rohanian, Omid; Taslimipoor, Shiva. - : Association for Computational Linguistics, 2019
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16
Empty Categories Help Parse the Overt
In: Proceedings of the Society for Computation in Linguistics (2019)
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17
Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles
In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.archives-ouvertes.fr/hal-01813395 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Jun 2018, New Orleans, United States. pp.401 - 406, ⟨10.18653/v1/N18-2064⟩ (2018)
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18
Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian
In: LREC 2018 Proceedings ; Language Resource and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-01856178 ; Language Resource and Evaluation Conference, ELRA, May 2018, Miyazaki, Japan ; http://www.lrec-conf.org/proceedings/lrec2018/pdf/600.pdf (2018)
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19
Quantifying training challenges of dependency parsers
In: Proceedings of the 27th International Conference on Computational Linguistics, ; International Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01907772 ; International Conference on Computational Linguistics, Aug 2018, Santa Fe, New Mexico, United States. pp.3191 - 3202 (2018)
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20
Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees
In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.archives-ouvertes.fr/hal-01813394 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, ACL, Jun 2018, New Orleans, United States. pp.413 - 419 (2018)
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