Triplet transformer network for multi-label document classification
Johannes Werner Melsbach, Sven Stahlmann, Stefan Hirschmeier, Detlef Schoder · 2022
Multi-label document classification is the task of assigning one or more labels to a document, and has become a common task in various businesses. Typically, current state-of-the-art models based on pretrained language models tackle this task without taking the textual information of label names into account, therefore omitting possibly valuable information. We present an approach that leverages this information stored in label names by reformulating the problem of multi label classification into a document similarity problem. To achieve this, we use a triplet transformer network that learns to embed labels and documents into a joint vector space. Our approach is fast at inference, classifying documents by determining the closest and therefore most similar labels. We evaluate our approach on a challenging real-world dataset of a German radio-broadcaster and find that our model provides competitive results compared to other established approaches.