  In this paper, we introduce the MLM (Multiple Languages and Modalities)
dataset - a new resource to train and evaluate multitask systems on samples in
multiple modalities and three languages. The generation process and inclusion
of semantic data provide a resource that further tests the ability for
multitask systems to learn relationships between entities. The dataset is
designed for researchers and developers who build applications that perform
multiple tasks on data encountered on the web and in digital archives. A second
version of MLM provides a geo-representative subset of the data with weighted
samples for countries of the European Union. We demonstrate the value of the
resource in developing novel applications in the digital humanities with a
motivating use case and specify a benchmark set of tasks to retrieve modalities
and locate entities in the dataset. Evaluation of baseline multitask and single
task systems on the full and geo-representative versions of MLM demonstrate the
challenges of generalising on diverse data. In addition to the digital
humanities, we expect the resource to contribute to research in multimodal
representation learning, location estimation, and scene understanding.
