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Prédire l'aspect linguistique en anglais au moyen de transformers
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In: à paraître : Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale ; Traitement Automatique des Langues Naturelles (TALN 2021) ; https://hal.archives-ouvertes.fr/hal-03265894 ; Traitement Automatique des Langues Naturelles (TALN 2021), 2021, Lille, France. pp.209-218 ; https://talnrecital2021.inria.fr/articles-acceptes/ (2021)
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Linguistically inspired morphological inflection with a sequence to sequence model ...
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Abstract:
Inflection is an essential part of every human language's morphology, yet little effort has been made to unify linguistic theory and computational methods in recent years. Methods of string manipulation are used to infer inflectional changes; our research question is whether a neural network would be capable of learning inflectional morphemes for inflection production in a similar way to a human in early stages of language acquisition. We are using an inflectional corpus (Metheniti and Neumann, 2020) and a single layer seq2seq model to test this hypothesis, in which the inflectional affixes are learned and predicted as a block and the word stem is modelled as a character sequence to account for infixation. Our character-morpheme-based model creates inflection by predicting the stem character-to-character and the inflectional affixes as character blocks. We conducted three experiments on creating an inflected form of a word given the lemma and a set of input and target features, comparing our architecture to ... : 13 pages, 6 figures ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2009.02073
https://dx.doi.org/10.48550/arxiv.2009.02073
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