Predicting the Duration and Type of ESG Impact from News Articles using Transformer-based Models

Core Concepts

The core message of this paper is to describe the different approaches explored by the Jetsons team for the Multi-Lingual ESG Impact Duration Inference (ML-ESG-3) shared task, which focuses on predicting the duration and type of the ESG impact of news articles. The team used a combination of traditional NLP techniques, data denoising, fine-tuning of multilingual language models, self-training, and ensemble methods to achieve top performance on the leaderboard for several languages.

Abstract

The paper presents the Jetsons team's approach to the ML-ESG-3 shared task, which aims to predict the duration and type of ESG impact from news articles in multiple languages.
For the impact duration classification task:

The team explored traditional NLP techniques like TF-IDF with logistic regression, SVM, and Random Forest classifiers as a baseline.
They also investigated denoising the data to evaluate the impact of removing noisy or less informative samples, which led to improved performance.
The team fine-tuned multilingual BERT-style models (XLM-RoBERTa and Longformer) on individual languages and the entire dataset.
They complemented direct fine-tuning with self-training using additional English and French ESG articles.
Translating all articles to English was also explored to simplify the impact duration task, and a DeBERTa-v3 model was fine-tuned on the translated data.
An ensemble of the best-performing models was used for the final submission.
For the impact level classification task:

The team fine-tuned the XLM-RoBERTa and Longformer models on the French and English data, both separately and combined.
The XLM-RoBERTa model outperformed the Longformer model in both languages.
The paper also provides an analysis of the results, including confusion matrices and a discussion of the best-performing models.

Stats

The training dataset consists of 2,059 news articles in four languages: 545 English, 661 French, 800 Korean, and 53 Japanese articles.
The dataset is highly skewed, with the majority of articles belonging to the "more than 5 years" impact duration class.
The French and English articles are also annotated with 'low', 'medium', or 'high' impact level classes.
The Korean dataset contains impact type annotations with the following classes: opportunity, risk, and cannot distinguish.

Quotes

"ESG (environment, social, and governance) related news can impact the performance and reputation of companies, investors, and regulators. One of the key challenges in ESG impact assessment is to estimate the duration of the ESG impact of a news article."
"Different news articles may have different levels of salience, credibility, and relevance for different stakeholders and thus may have different effects on their behavior and outcomes."

Key Insights Distilled From

Jetsons at FinNLP 2024

by Parag Pravin... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00386.pdf

Deeper Inquiries

How can the impact duration and level classification models be further improved, especially for languages with smaller datasets like Japanese?

To enhance the performance of impact duration and level classification models, particularly for languages with limited datasets like Japanese, several strategies can be implemented:

Data Augmentation: Utilize data augmentation techniques such as back-translation, synonym replacement, or data synthesis to increase the size of the Japanese dataset. This can help in providing more diverse examples for the model to learn from.

Transfer Learning: Implement transfer learning by pre-training the model on a larger dataset in a similar language or domain before fine-tuning on the Japanese dataset. This can help the model capture more nuanced patterns and improve generalization.

Ensemble Methods: Combine predictions from multiple models trained on different subsets of the data or using different architectures to create a more robust and accurate ensemble model for Japanese.

Semi-supervised Learning: Incorporate semi-supervised learning techniques where the model learns from both labeled and unlabeled data. This can be particularly useful in scenarios where labeled data is scarce.

Domain-specific Features: Integrate domain-specific features related to ESG, financial data, or company-specific information that can provide additional context and improve the model's understanding of the impact duration and level in Japanese news articles.

By implementing these strategies, the impact duration and level classification models for languages with smaller datasets like Japanese can be further enhanced in terms of accuracy and performance.

How can the insights from this work be applied to other domains beyond ESG, where understanding the impact and relevance of news articles is crucial?

The insights gained from this work on ESG impact duration and level classification can be extrapolated and applied to various other domains where understanding the impact and relevance of news articles is essential. Some ways to apply these insights to other domains include:

Topic Classification: Utilize the fine-tuning strategies and ensemble methods developed for ESG impact classification to classify news articles into different topics or categories in domains such as healthcare, technology, or politics.

Sentiment Analysis: Adapt the models and techniques used for impact duration and level classification to perform sentiment analysis on news articles, social media posts, or customer reviews in industries like marketing, customer service, or brand management.

Risk Assessment: Apply the data augmentation and semi-supervised learning approaches to assess risks associated with financial investments, market trends, or regulatory changes by analyzing news articles and financial reports.

Event Detection: Implement the language models and fine-tuning strategies to detect and classify significant events or incidents reported in news articles across various domains like sports, entertainment, or natural disasters.

Market Intelligence: Utilize the models to extract insights from news articles related to market trends, competitor analysis, or consumer behavior in industries such as retail, e-commerce, or real estate.

By leveraging the methodologies and techniques developed for ESG impact analysis, organizations can adapt and apply these insights to a wide range of domains where understanding the impact and relevance of news articles is crucial for decision-making and strategic planning.