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Most of the available user information can be represented as a sequence oftimestamped events. Each event is assigned a set of categorical labels whosefuture structure is of great interest. For instance, our goal is to predict agroup of items in the next customer's purchase or tomorrow's clienttransactions. This is a multi-label classification problem for sequential data.Modern approaches focus on transformer architecture for sequential dataintroducing self-attention for the elements in a sequence. In that case, wetake into account events' time interactions but lose information on labelinter-dependencies. Motivated by this shortcoming, we propose leveraging aself-attention mechanism over labels preceding the predicted step. As ourapproach is a Label-Attention NETwork, we call it LANET. Experimental evidencesuggests that LANET outperforms the established models' performance and greatlycaptures interconnections between labels. For example, the micro-AUC of ourapproach is $0.9536$ compared to $0.7501$ for a vanilla transformer. We providean implementation of LANET to facilitate its wider usage.

Label Attention Network for sequential multi-label classification: you were looking at a wrong self-attention