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AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization ...
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Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations
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In: Proceedings of the 28th International Conference on Computational Linguistics ; 28th International Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03168039 ; 28th International Conference on Computational Linguistics, Dec 2020, Barcelona (on line), Spain. ⟨10.18653/v1/2020.coling-main.225⟩ (2020)
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Multitask Easy-First Dependency Parsing ...
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Abstract:
In this paper we present a parsing model for projective dependency trees which takes advantage of the existence of complementary dependency annotations which is the case in Arabic, with the availability of CATiB and UD treebanks. Our system performs syntactic parsing according to both annotation types jointly as a sequence of arc-creating operations, and partially created trees for one annotation are also available to the other as features for the score function. This method gives error reduction of 9.9% on CATiB and 6.1% on UD compared to a strong baseline, and ablation tests show that the main contribution of this reduction is given by sharing tree representation between tasks, and not simply sharing BiLSTM layers as is often performed in NLP multitask systems. ...
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Keyword:
Computer and Information Science; Natural Language Processing; Neural Network
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URL: https://dx.doi.org/10.48448/bh61-zb18 https://underline.io/lecture/6319-multitask-easy-first-dependency-parsing
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Proceedings of the Fifth Arabic Natural Language Processing Workshop
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AraWEAT: Multidimensional analysis of biases in Arabic word embeddings
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Deep Lexical Segmentation and Syntactic Parsing in the Easy-First Dependency Framework
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In: NAACL ; https://hal.archives-ouvertes.fr/hal-01494125 ; NAACL, 2016, San Diego, United States (2016)
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Pattern mining and CRF for symptoms recognition in biomedical texts ; Fouille de motifs et CRF pour la reconnaissance de symptômes dans les textes biomédicaux
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In: 23e conférence sur le Traitement Automatique des Langues Naturelles (TALN’16) ; https://halshs.archives-ouvertes.fr/halshs-01727081 ; 23e conférence sur le Traitement Automatique des Langues Naturelles (TALN’16), Jul 2016, Paris, France. pp.194-206 (2016)
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Weakly-supervised Symptom Recognition for Rare Diseases in Biomedical Text
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In: 15th International Symposium on Intelligent Data Analysis ; https://halshs.archives-ouvertes.fr/halshs-01727071 ; 15th International Symposium on Intelligent Data Analysis, Oct 2016, Stockholm, Sweden (2016)
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LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficient
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In: SemEval 2014 ; https://hal.archives-ouvertes.fr/hal-01068277 ; SemEval 2014, Aug 2014, Dublin, Ireland. pp.400-405 (2014)
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Discriminative Alignment Models For Statistical Machine Translation ; Modèles Discriminants d'Alignement Pour La Traduction Automatique Statistique
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In: https://tel.archives-ouvertes.fr/tel-00720250 ; Other [cs.OH]. Université Paris Sud - Paris XI, 2012. English. ⟨NNT : 2012PA112104⟩ (2012)
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Designing an improved discriminative word aligner
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In: International Conference on Intelligent Text Processing and Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01960730 ; International Conference on Intelligent Text Processing and Computational Linguistics, Jan 2011, Tokyo, Japan (2011)
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