Knowledge informed sustainability detection from short financial texts

There is a global trend for responsible investing and the need for developing automated methods for analyzing and Environmental, Social and Governance (ESG) related elements in financial texts is raising. In this work we propose a solution to the FinSim4-ESG task, consisting of binary classification of sentences into sustainable or unsustainable. We propose a novel knowledge-based latent heterogeneous representation that is based on knowledge from taxonomies and knowledge graphs and multiple contemporary document representations. We hypothesize that an approach based on a combination of knowledge and document representations can introduce significant improvement over conventional document representation approaches. We consider ensembles on classifier as well on representation level late-fusion and early fusion. The proposed approaches achieve competitive accuracy of 89 and are 5.85 behind the best achieved score.