On the Transferability of Neural Models of Morphological Analogies
First published in
Number in series
Communications in Computer and Information Science 1524: 76-89 (2021)
Springer International Publishing
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.