# A Simple Joint Model for Improved Contextual Neural Lemmatization

arXiv: https://arxiv.org/abs/1904.02306 · HTML: https://mlsanity.com/paper/1904.02306/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered](https://mlsanity.com/paper/2105.12428/) (2021)
- [Morphological Processing of Low-Resource Languages: Where We Are and What's Next](https://mlsanity.com/paper/2203.08909/) (2022)
- [On the Role of Morphological Information for Contextual Lemmatization](https://mlsanity.com/paper/2302.00407/) (2023)
- [Computational Morphology with Neural Network Approaches](https://mlsanity.com/paper/2105.09404/) (2021)
- [Transformers on Multilingual Clause-Level Morphology](https://mlsanity.com/paper/2211.01736/) (2022)
- [Evaluating Shortest Edit Script Methods for Contextual Lemmatization](https://mlsanity.com/paper/2403.16968/) (2024)
- [Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data](https://mlsanity.com/paper/2510.07434/) (2025)

## Abstract (arXiv)

English verbs have multiple forms. For instance, talk may also appear as talks, talked or talking, depending on the context. The NLP task of lemmatization seeks to map these diverse forms back to a canonical one, known as the lemma. We present a simple joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. Our paper describes the model in addition to training and decoding procedures. Error analysis indicates that joint morphological tagging and lemmatization is especially helpful in low-resource lemmatization and languages that display a larger degree of morphological complexity. Code and pre-trained models are available at https://sigmorphon.github.io/sharedtasks/2019/task2/.