  Most of the available user information can be represented as a sequence of
timestamped events. Each event is assigned a set of categorical labels whose
future structure is of great interest. For instance, our goal is to predict a
group of items in the next customer's purchase or tomorrow's client
transactions. This is a multi-label classification problem for sequential data.
Modern approaches focus on transformer architecture for sequential data
introducing self-attention for the elements in a sequence. In that case, we
take into account events' time interactions but lose information on label
inter-dependencies. Motivated by this shortcoming, we propose leveraging a
self-attention mechanism over labels preceding the predicted step. As our
approach is a Label-Attention NETwork, we call it LANET. Experimental evidence
suggests that LANET outperforms the established models' performance and greatly
captures interconnections between labels. For example, the micro-AUC of our
approach is $0.9536$ compared to $0.7501$ for a vanilla transformer. We provide
an implementation of LANET to facilitate its wider usage.
