On the Transferability of Neural Models of Morphological Analogies

• URL: http://arxiv.org/abs/2108.03938v1
• Date: Mon, 9 Aug 2021 11:08:33 GMT
• Title: On the Transferability of Neural Models of Morphological Analogies
• Authors: Safa Alsaidi, Amandine Decker, Puthineath Lay, Esteban Marquer, Pierre-Alexandre Murena, Miguel Couceiro
• Abstract summary: In this paper, we focus on morphological tasks and we propose a deep learning approach to detect morphological analogies. We present an empirical study to see how our framework transfers across languages, and that highlights interesting similarities and differences between these languages. In view of these results, we also discuss the possibility of building a multilingual morphological model.
• Score: 7.89271130004391
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Analogical proportions are statements expressed in the form "A is to B as C is to D" and are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). In this paper, we focus on morphological tasks and we propose a deep learning approach to detect morphological analogies. We present an empirical study to see how our framework transfers across languages, and that highlights interesting similarities and differences between these languages. In view of these results, we also discuss the possibility of building a multilingual morphological model.

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