# Tackling Morphological Analogies Using Deep Learning -- Extended Version

arXiv: https://arxiv.org/abs/2111.05147 · HTML: https://mlsanity.com/paper/2111.05147/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learning](https://mlsanity.com/paper/1401.3427/) (2014)
- [Evaluating vector-space models of analogy](https://mlsanity.com/paper/1705.04416/) (2017)
- [A Neural Approach for Detecting Morphological Analogies](https://mlsanity.com/paper/2108.03945/) (2021)
- [On the Transferability of Neural Models of Morphological Analogies](https://mlsanity.com/paper/2108.03938/) (2021)

## Built on by (most cited first, 2 of 2)

- [Solving morphological analogies: from retrieval to generation](https://mlsanity.com/paper/2303.18062/) (2023)
- [In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs](https://mlsanity.com/paper/2605.05931/) (2026)

## Abstract (arXiv)

Analogical proportions are statements of the form "A is to B as C is to D". They constitute an inference tool that provides a logical framework to address learning, transfer, and explainability concerns and that finds useful applications in artificial intelligence and natural language processing. In this paper, we address two problems, namely, analogy detection and resolution in morphology. Multiple symbolic approaches tackle the problem of analogies in morphology and achieve competitive performance. We show that it is possible to use a data-driven strategy to outperform those models. We propose an approach using deep learning to detect and solve morphological analogies. It encodes structural properties of analogical proportions and relies on a specifically designed embedding model capturing morphological characteristics of words. We demonstrate our model's competitive performance on analogy detection and resolution over multiple languages. We provide an empirical study to analyze the impact of balancing training data and evaluate the robustness of our approach to input perturbation.