  Many Machine Reading and Natural Language Understanding tasks require reading
supporting text in order to answer questions. For example, in Question
Answering, the supporting text can be newswire or Wikipedia articles; in
Natural Language Inference, premises can be seen as the supporting text and
hypotheses as questions. Providing a set of useful primitives operating in a
single framework of related tasks would allow for expressive modelling, and
easier model comparison and replication. To that end, we present Jack the
Reader (Jack), a framework for Machine Reading that allows for quick model
prototyping by component reuse, evaluation of new models on existing datasets
as well as integrating new datasets and applying them on a growing set of
implemented baseline models. Jack is currently supporting (but not limited to)
three tasks: Question Answering, Natural Language Inference, and Link
Prediction. It is developed with the aim of increasing research efficiency and
code reuse.
