  We introduce ELIT, the Emory Language and Information Toolkit, which is a
comprehensive NLP framework providing transformer-based end-to-end models for
core tasks with a special focus on memory efficiency while maintaining
state-of-the-art accuracy and speed. Compared to existing toolkits, ELIT
features an efficient Multi-Task Learning (MTL) model with many downstream
tasks that include lemmatization, part-of-speech tagging, named entity
recognition, dependency parsing, constituency parsing, semantic role labeling,
and AMR parsing. The backbone of ELIT's MTL framework is a pre-trained
transformer encoder that is shared across tasks to speed up their inference.
ELIT provides pre-trained models developed on a remix of eight datasets. To
scale up its service, ELIT also integrates a RESTful Client/Server combination.
On the server side, ELIT extends its functionality to cover other tasks such as
tokenization and coreference resolution, providing an end user with agile
research experience. All resources including the source codes, documentation,
and pre-trained models are publicly available at
https://github.com/emorynlp/elit.
