Knowledge informed sustainability detection from short financial texts
Boshko Koloski, Syrielle Montariol, Matthew Purver, Senja Pollak · 2022
Nowadays in the finance world, there is a global trend for responsible investing, linked with a growing need for developing automated methods for analysing Environmental, Social and Governance (ESG) related elements in financial texts.In this work we propose a solution to the FinSim4-ESG task, consisting in classifying sentences from financial reports as sustainable or unsustainable.We propose a novel knowledge-based latent heterogeneous representation that relies on knowledge from taxonomies, 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 perform ensembling, both at the classifier level and at the representation level (late-fusion and early-fusion).The proposed approaches achieve competitive accuracy of 89% and are 5.85% behind the best score in the shared task.