In this paper, we present our solutions to the ML-ESG-2 shared task which is co-located with the FinNLP workshop at IJCNLP-AACL-2023. The task proposes an objective of binary classification of ESG-related news based on what type of impact they can have on a company - Risk or Opportunity. We report the results of three systems, which ranked 2nd, 9th, and 10th in the final leaderboard for the English language, with the best solution achieving over 0.97 in F1 score.

This paper describes a system developed for the Social Media Mining for Health 2020 shared task. Our team participated in the second subtask for Russian language creating a system to detect adverse drug reaction presence in a text. For our submission, we exploited an ensemble model architecture, combining BERT’s extension for Russian language, Logistic Regression and domain-specific preprocessing pipeline. Our system was ranked first among others, achieving F-score of 0.51.