  In a classification task, dealing with text snippets and metadata usually
requires dealing with multimodal approaches. When those metadata are textual,
it is tempting to use them intrinsically with a pre-trained transformer, in
order to leverage the semantic information encoded inside the model. This paper
describes how to improve a humanitarian classification task by adding the
crisis event type to each tweet to be classified. Based on additional
experiments of the model weights and behavior, it identifies how the proposed
neural network approach is partially over-fitting the particularities of the
Crisis Benchmark, to better highlight how the model is still undoubtedly
learning to use and take advantage of the metadata's textual semantics.
