# Modeling Composite Labels for Neural Morphological Tagging

arXiv: https://arxiv.org/abs/1810.08815 · HTML: https://mlsanity.com/paper/1810.08815/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2018.

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

- [Morpheme Boundary Detection & Grammatical Feature Prediction for Gujarati : Dataset & Model](https://mlsanity.com/paper/2112.09860/) (2021)
- [Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech](https://mlsanity.com/paper/2504.00780/) (2025)

## Abstract (arXiv)

Neural morphological tagging has been regarded as an extension to POS tagging task, treating each morphological tag as a monolithic label and ignoring its internal structure. We propose to view morphological tags as composite labels and explicitly model their internal structure in a neural sequence tagger. For this, we explore three different neural architectures and compare their performance with both CRF and simple neural multiclass baselines. We evaluate our models on 49 languages and show that the neural architecture that models the morphological labels as sequences of morphological category values performs significantly better than both baselines establishing state-of-the-art results in morphological tagging for most languages.