  This paper details LTG-Oslo team's participation in the sentiment track of
the NEGES 2019 evaluation campaign. We participated in the task with a
hierarchical multi-task network, which used shared lower-layers in a deep
BiLSTM to predict negation, while the higher layers were dedicated to
predicting document-level sentiment. The multi-task component shows promise as
a way to incorporate information on negation into deep neural sentiment
classifiers, despite the fact that the absolute results on the test set were
relatively low for a binary classification task.
