  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/.
