# A Simple Joint Model for Improved Contextual Neural Lemmatization

## TL;DR

This paper introduces a simple joint neural model for lemmatization and morphological tagging that improves accuracy across 20 languages, especially in low-resource and morphologically complex languages.

## Contribution

A novel joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on multiple languages.

## Key findings

- Joint modeling improves lemmatization accuracy in low-resource languages.
- The model performs well on morphologically complex languages.
- Code and models are publicly available.

## Abstract

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/.