# On the Transferability of Neural Models of Morphological Analogies

arXiv: https://arxiv.org/abs/2108.03938 · HTML: https://mlsanity.com/paper/2108.03938/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learning](https://mlsanity.com/paper/1401.3427/) (2014)

## Built on by (most cited first, 4 of 4)

- [Galois theory for analogical classifiers](https://mlsanity.com/paper/2205.04593/) (2022)
- [Solving morphological analogies: from retrieval to generation](https://mlsanity.com/paper/2303.18062/) (2023)
- [Tackling Morphological Analogies Using Deep Learning -- Extended Version](https://mlsanity.com/paper/2111.05147/) (2021)
- [Any four real numbers are on all fours with analogy](https://mlsanity.com/paper/2407.18770/) (2024)

## Abstract (arXiv)

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.