# Morphological analysis using a sequence decoder

arXiv: https://arxiv.org/abs/1805.07946 · HTML: https://mlsanity.com/paper/1805.07946/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 3 of 3)

- [Computational Morphology with Neural Network Approaches](https://mlsanity.com/paper/2105.09404/) (2021)
- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)
- [Neural Morphological Tagging for Nguni Languages](https://mlsanity.com/paper/2505.12949/) (2025)

## Abstract (arXiv)

We introduce Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence. The encoder turns the relevant information about the word and its context into a fixed size vector representation and the decoder generates the sequence of characters for the lemma followed by a sequence of individual morphological features. We show that generating morphological features individually rather than as a combined tag allows the model to handle rare or unseen tags and outperform whole-tag models. In addition, generating morphological features as a sequence rather than e.g. an unordered set allows our model to produce an arbitrary number of features that represent multiple inflectional groups in morphologically complex languages. We obtain state-of-the art results in nine languages of different morphological complexity under low-resource, high-resource and transfer learning settings. We also introduce TrMor2018, a new high accuracy Turkish morphology dataset. Our Morse implementation and the TrMor2018 dataset are available online to support future researchfootnoteSee urlhttps://github.com/ai-ku/Morse.jl for a Morse implementation in Julia/Knet citeknet2016mlsys and urlhttps://github.com/ai-ku/TrMor2018 for the new Turkish dataset..