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Multitask Pointer Network for Multi-Representational Parsing
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Joint learning of morphology and syntax with cross-level contextual information flow
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In: 2022 ; 1 ; 33 (2022)
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Analyse en dépendances du français avec des plongements contextualisés
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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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To be or not to be adultlike in syntax: An experimental study of language acquisition and processing in children ...
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PLPrepare: A Grammar Checker for Challenging Cases
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In: Electronic Theses and Dissertations (2021)
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To be or not to be adultlike in syntax: An experimental study of language acquisition and processing in children
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Resourceful at Any Size: A Predictive Methodology Using Linguistic Corpus Metrics for Multi-Source Training in Neural Dependency Parsing
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Treebank embedding vectors for out-of-domain dependency parsing
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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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Abstract:
A recent advance in monolingual dependency parsing is the idea of a treebank embedding vector, which allows all treebanks for a particular language to be used as training data while at the same time allowing the model to prefer training data from one treebank over others and to select the preferred treebank at test time. We build on this idea by 1) introducing a method to predict a treebank vector for sentences that do not come from a treebank used in training, and 2) exploring what happens when we move away from predefined treebank embedding vectors during test time and instead devise tailored interpolations. We show that 1) there are interpolated vectors that are superior to the predefined ones, and 2) treebank vectors can be predicted with sufficient accuracy, for nine out of ten test languages, to match the performance of an oracle approach that knows the most suitable predefined treebank embedding for the test set.
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Keyword:
Computational linguistics; dependency parsing
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URL: http://doras.dcu.ie/24861/
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Bootstrap methods for multi-task dependency parsing in low-resource conditions ; Méthodes d’amorçage pour l’analyse en dépendances de langues peu dotées
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In: https://tel.archives-ouvertes.fr/tel-03477961 ; Linguistics. Université Paris sciences et lettres, 2020. English. ⟨NNT : 2020UPSLE027⟩ (2020)
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Extrinsic Evaluation of French Dependency Parsers on a Specialized Corpus: Comparison of Distributional Thesauri
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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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On understanding character-level models for representing morphology ...
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On understanding character-level models for representing morphology
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Self attended stack pointer networks for learning long term dependencies
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Annotation syntaxique automatique de la partie orale du ORFÉO
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In: Langages, N 219, 3, 2020-08-11, pp.87-102 (2020)
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