Label Attention Network for sequential multi-label classification

Multi-label classification is a natural problem statement for sequential data. We might be interested in the items of the next order by a customer, or types of financial transactions that will occur tomorrow. Most modern approaches focus on transformer architecture for multi-label classification, introducing self-attention for the elements of a sequence with each element being a multi-label vector and supplementary information. However, in this way we loose local information related to interconnections between particular labels. We propose instead to use a self-attention mechanism over labels preceding the predicted step. Conducted experiments suggest that such architecture improves the model performance and provides meaningful attention between labels. The metric such as micro-AUC of our label attention network is $0.9847$ compared to $0.7390$ for vanilla transformers benchmark.