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SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding ...
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Improving Tokenisation by Alternative Treatment of Spaces ...
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Investigating alignment interpretability for low-resource NMT [<Journal>]
Boito, Marcely Zanon [Verfasser]; Villavicencio, Aline [Verfasser]; Besacier, Laurent [Verfasser]
DNB Subject Category Language
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Investigating alignment interpretability for low-resource NMT
In: ISSN: 0922-6567 ; EISSN: 1573-0573 ; Machine Translation ; https://hal.archives-ouvertes.fr/hal-03139744 ; Machine Translation, Springer Verlag, 2021, &#x27E8;10.1007/s10590-020-09254-w&#x27E9; (2021)
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AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models ...
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Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings ...
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Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels ...
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The Role of negative information when learning dense word vectors ; O papel da informação negativa na aprendizagem de vetores palavra densos
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Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
In: Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), ; SLTU-CCURL workshop, LREC 2020 ; https://hal.archives-ouvertes.fr/hal-02895907 ; SLTU-CCURL workshop, LREC 2020, May 2020, Marseille, France (2020)
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Annotated corpora and tools of the PARSEME Shared Task on Semi-Supervised Identification of Verbal Multiword Expressions (edition 1.2)
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Investigating Language Impact in Bilingual Approaches for Computational Language Documentation ...
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Empirical Evaluation of Sequence-to-Sequence Models for Word Discovery in Low-resource Settings
In: Interspeech 2019 ; https://hal.archives-ouvertes.fr/hal-02193867 ; Interspeech 2019, Sep 2019, Graz, Austria (2019)
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Unsupervised Compositionality Prediction of Nominal Compounds
In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02318196 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (1), pp.1-57. &#x27E8;10.1162/coli_a_00341&#x27E9; (2019)
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How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages
In: Journées Scientifiques du Groupement de Recherche: Linguistique Informatique, Formelle et de Terrain (LIFT). ; https://hal.archives-ouvertes.fr/hal-02895895 ; Journées Scientifiques du Groupement de Recherche: Linguistique Informatique, Formelle et de Terrain (LIFT)., Nov 2019, Orléans, France (2019)
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How Does Language Influence Documentation Workflow? Unsupervised Word Discovery Using Translations in Multiple Languages ...
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CogniVal: A Framework for Cognitive Word Embedding Evaluation
In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) (2019)
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Unsupervised Compositionality Prediction of Nominal Compounds
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A small Griko-Italian speech translation corpus
In: 6th international workshop on spoken language technologies for under-resourced languages(SLTU'18) ; https://hal.archives-ouvertes.fr/hal-01962528 ; 6th international workshop on spoken language technologies for under-resourced languages(SLTU'18), Aug 2018, New Delhi, India (2018)
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Unsupervised Word Segmentation from Speech with Attention
In: Interspeech 2018 ; https://hal.archives-ouvertes.fr/hal-01818092 ; Interspeech 2018, Sep 2018, Hyderabad, India (2018)
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Language, Cognition, and Computational Models
Poibeau, Thierry; Villavicencio, Aline. - : HAL CCSD, 2018. : Cambridge University Press, 2018
In: https://hal.archives-ouvertes.fr/hal-01722351 ; Cambridge University Press, 2018 ; https://www.cambridge.org/core/books/language-cognition-and-computational-models/90CC7DBA6CADB1FE361266D311CB4413 (2018)
Abstract: International audience ; How do infants learn a language? Why and how do languages evolve? How do we understand a sentence? This book explores these questions using recent computational models that shed new light on issues related to language and cognition. The chapters in this collection propose original analyses of specific problems and develop computational models that have been tested and evaluated on real data. Featuring contributions from a diverse group of experts, this interdisciplinary book bridges the gap between natural language processing and cognitive sciences. It is divided into three sections, focusing respectively on models of neural and cognitive processing, data driven methods, and social issues in language evolution. This book will be useful to any researcher and advanced student interested in the analysis of the links between the brain and the language faculty.Read more at http://www.cambridge.org/gb/academic/subjects/computer-science/artificial-intelligence-and-natural-language-processing/language-cognition-and-computational-models#U0qsxUGLEojBUFe6.99
Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [SCCO.COMP]Cognitive science/Computer science; [SCCO.LING]Cognitive science/Linguistics; [SHS.INFO]Humanities and Social Sciences/Library and information sciences; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; Cognition; Computational Models; Language; Machine learning
URL: https://hal.archives-ouvertes.fr/hal-01722351
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