A low resource framework for Multi-lingual ESG Impact Type Identification (2023)

Abstract: With the growing interest in Green Investing, Environmental, Social, and Governance (ESG) factors related to Institutions and financial entities has become extremely important for investors. While the classification of potential ESG factors is an important issue, identifying whether the factors positively or negatively impact the Institution is also a key aspect to consider while making evaluations for ESG scores. This paper presents our solution to identify ESG impact types in four languages (English, Chinese, Japanese, French) released as shared tasks during the FinNLP workshop at the IJCNLP-AACL-2023 conference. We use a combination of translation, masked language modeling, paraphrasing, and classification to solve this problem and use a generalized pipeline that performs well across all four languages. Our team ranked 1st in the Chinese and Japanese sub-tasks.

## introduction in recent times, the focus on institutions' environmental, social, and governance factors (esg) has garnered increased interest from the global investment and corporate governance communities. people have also grown to be socially responsible and environmentally conscious while investing. esg serves as a third dimension beyond risk and return. research also indicates that institutions with better esg performance directly correlate to better stock performance and risk management @xcite . keeping this in mind, many rating agencies quantify the nature and impact of esg aspects of an institution and publish ratings @xcite . apart from esg investing, impact investing @xcite has also gained traction where investors, instead of investing solely based on esg benefits, would look for a combination of better returns as well as a positive influence in society. hence, impact identification is crucial to determine whether statements are an opportunity or a risk for the institution. most of the scoring processes involved in esg and impact assessments are extremely timeconsuming and require expert involvement and manual annotations. to automate this, we propose a generalized pipeline capable of predicting the impact types of esg-related news articles (as shown in figure 1 ). this generalized pipeline can be scaled to other low-resource datasets as well. the labels primarily indicate if the given news is an opportunity or a risk from the esg aspect. in this shared task, we participated in all four languages and were ranked 1 st in chinese and japanese sub-tasks, 4 th in french, and 7 th in english. ## related work with the advent of green investing, many approaches and models have been developed to automate processes in financial and esg-based nlp research, including the development of models like finbert @xcite , esgbert @xcite , etc. while there has been much work on esg-type classifications, including on multilingual datasets, more work needs to be done on impacttype classifications. finnlp 2023 @xcite focuses on a similar task where participants were required to classify multilingual data into the esg issue type, where the best results were obtained by using language-specific bert models along with data augmentations using large language models. furthermore, it's important to note that extensive research has been conducted on sentiment analysis @xcite , which can be considered a fundamental aspect of impact identification. attempts have also been made for impact identification in chinese @xcite and japanese (kannan and seki, 2023). ## task description the task is primarily a classification task where given a text, classify whether the text poses a risk or an opportunity for the company. as shown in figure 1 , there are multiple languages with differences in classes. ## data the dataset primarily contains news articles collected from four different languages, english (en), chinese (zh), japanese (ja), and french (fr), along with their impact types. given that the dataset across languages is small, and the classwise distribution is highly skewed. to overcome these challenges, we use a combination of translation and data paraphrasing on minority classes. ## approaches we primarily used encoder-based models for this classification task. given the limited sample size of the dataset, variations in languages, and disparity with class distribution among different languages, we tried to make a pipeline that accounted for such differences and performed consistently well across all languages. we tried a variety of approaches like masked language modelling (mlm), paraphrasing for augmenting the minority classes, translation, and multilingual models and used a combination to finalize our pipeline based on empirical experiments. all of the experiments have been run using a batch size of 32, a learning rate of 2e -5 , weight decay of 0.01, and for ten epochs. the reported metrics are based on 80 : 20 train-test set splits with a constant random seed and not on the final validation sets used for the leaderboard. the code, data, and models used for inferences are available at the link. ## final system description for the final system that was used, based on the empirical studies performed above, we used a pipeline that initially translated all of the given text into english using google translate. then we use the t5-based paraphraser @xcite to generate new minority class instances. we also use an esg corpus to initially pre-train a model on this corpus and then fine-tune it for classification on the translated and augmented dataset. figure 2 shows the exact process. we also performed more experiments to decide which models best performed on the english dataset and chose bert-base-cased @xcite , finbert @xcite , and finberttone @xcite . we used the same models for the other languages as well. the model hyper-parameters are the same as mentioned in the methodology. we observe that despite using a generalized pipeline and models for all the languages, the results are good. ## conclusion comparing the performance of our models with that of other participants, we conclude that our models performed consistently well. we outperformed all other teams in the chinese and japanese sub-tasks. one unique feature is despite four different languages, we were able to use the same pipeline and same set of models and achieve consistently good results across languages, which leads us to believe that the pipeline is performant for low resource settings. all of the data generated and code used can be accessed here.