  Technology-assisted review (TAR) is an important industrial application of
information retrieval (IR) and machine learning (ML). While a small TAR
research community exists, the complexity of TAR software and workflows is a
major barrier to entry. Drawing on past open source TAR efforts, as well as
design patterns from the IR and ML open source software, we present an open
source Python framework for conducting experiments on TAR algorithms. Key
characteristics of this framework are declarative representations of workflows
and experiment plans, the ability for components to play variable numbers of
workflow roles, and state maintenance and restart capabilities. Users can draw
on reference implementations of standard TAR algorithms while incorporating
novel components to explore their research interests. The framework is
available at https://github.com/eugene-yang/tarexp.
