# Recent advancements in computational morphology : A comprehensive survey

arXiv: https://arxiv.org/abs/2406.05424 · HTML: https://mlsanity.com/paper/2406.05424/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 133 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection](https://mlsanity.com/paper/1910.11493/) (2019)
- [UniMorph 2.0: Universal Morphology](https://mlsanity.com/paper/1810.11101/) (2018)
- [Morphology Matters: A Multilingual Language Modeling Analysis](https://mlsanity.com/paper/2012.06262/) (2020)
- [KinyaBERT: a Morphology-aware Kinyarwanda Language Model](https://mlsanity.com/paper/2203.08459/) (2022)
- [The SIGMORPHON 2022 Shared Task on Morpheme Segmentation](https://mlsanity.com/paper/2206.07615/) (2022)
- [Cross-lingual, Character-Level Neural Morphological Tagging](https://mlsanity.com/paper/1708.09157/) (2017)
- [UDPipe at SIGMORPHON 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Merging](https://mlsanity.com/paper/1908.06931/) (2019)
- [Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages](https://mlsanity.com/paper/1804.06024/) (2018)
- [Evaluating historical text normalization systems: How well do they generalize?](https://mlsanity.com/paper/1804.02545/) (2018)
- [Modeling Target-Side Inflection in Neural Machine Translation](https://mlsanity.com/paper/1707.06012/) (2017)
- [An Unsupervised Method for Uncovering Morphological Chains](https://mlsanity.com/paper/1503.02335/) (2015)
- [Improving historical spelling normalization with bi-directional LSTMs and multi-task learning](https://mlsanity.com/paper/1610.07844/) (2016)
- [Neural Factor Graph Models for Cross-lingual Morphological Tagging](https://mlsanity.com/paper/1805.04570/) (2018)
- [LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNs](https://mlsanity.com/paper/1808.03703/) (2018)
- [What do character-level models learn about morphology? The case of dependency parsing](https://mlsanity.com/paper/1808.09180/) (2018)
- [Joint Semantic Synthesis and Morphological Analysis of the Derived Word](https://mlsanity.com/paper/1701.00946/) (2017)
- [Joint Lemmatization and Morphological Tagging with LEMMING](https://mlsanity.com/paper/2405.18308/) (2024)
- [Paradigm Completion for Derivational Morphology](https://mlsanity.com/paper/1708.09151/) (2017)
- [Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered](https://mlsanity.com/paper/2105.12428/) (2021)
- [Neural Morphological Tagging from Characters for Morphologically Rich Languages](https://mlsanity.com/paper/1606.06640/) (2016)
- [Context-Aware Prediction of Derivational Word-forms](https://mlsanity.com/paper/1702.06675/) (2017)
- [Morphological Segmentation Inside-Out](https://mlsanity.com/paper/1911.04916/) (2019)

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

- [The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?](https://mlsanity.com/paper/2601.07220/) (2026)
- [Comonadic Morphophonology: A Compositional Framework for Context-Dependent Morphological Rules in Finnish](https://mlsanity.com/paper/2605.28484/) (2026)

## Abstract (arXiv)

Computational morphology handles the language processing at the word level. It is one of the foundational tasks in the NLP pipeline for the development of higher level NLP applications. It mainly deals with the processing of words and word forms. Computational Morphology addresses various sub problems such as morpheme boundary detection, lemmatization, morphological feature tagging, morphological reinflection etc. In this paper, we present exhaustive survey of the methods for developing computational morphology related tools. We survey the literature in the chronological order starting from the conventional methods till the recent evolution of deep neural network based approaches. We also review the existing datasets available for this task across the languages. We discuss about the effectiveness of neural model compared with the traditional models and present some unique challenges associated with building the computational morphology tools. We conclude by discussing some recent and open research issues in this field.