  We introduce Trankit, a light-weight Transformer-based Toolkit for
multilingual Natural Language Processing (NLP). It provides a trainable
pipeline for fundamental NLP tasks over 100 languages, and 90 pretrained
pipelines for 56 languages. Built on a state-of-the-art pretrained language
model, Trankit significantly outperforms prior multilingual NLP pipelines over
sentence segmentation, part-of-speech tagging, morphological feature tagging,
and dependency parsing while maintaining competitive performance for
tokenization, multi-word token expansion, and lemmatization over 90 Universal
Dependencies treebanks. Despite the use of a large pretrained transformer, our
toolkit is still efficient in memory usage and speed. This is achieved by our
novel plug-and-play mechanism with Adapters where a multilingual pretrained
transformer is shared across pipelines for different languages. Our toolkit
along with pretrained models and code are publicly available at:
https://github.com/nlp-uoregon/trankit. A demo website for our toolkit is also
available at: http://nlp.uoregon.edu/trankit. Finally, we create a demo video
for Trankit at: https://youtu.be/q0KGP3zGjGc.
