Emotion-based Morality in T agalog and E nglish Scenarios ( EM o TES -3 K ): A Parallel Corpus for Explaining (Im)morality of Actions (2023)

Abstract: Grasping morality is vital in AI systems, particularly as they become more prevalent in human-focused applications. Yet, research is scarce on this topic. This study presents the Emotion-based Morality in Tagalog and English Scenarios (EMoTES-3K), a collection that shows commonsense morality in both Filipino and English. This dataset is instrumental for analyzing moral decisions in various situations and their justifications. Our tests show that EMoTES-3K is effective for moral text categorization, with the fine-tuned RoBERTa model scoring 94.95% accuracy in English and 88.53% in Filipino. The dataset also excels in text generation tasks, as shown by fine-tuning the FLAN-T5 model to produce clear moral explanations. However, the model faces challenges when dealing with actions that have mixed moral implications. This work not only bridges the gap in moral reasoning datasets for languages like Filipino but also sets the stage for future research in commonsense moral reasoning in artificial intelligence.

## introduction moral reasoning, a cornerstone of human cognition, allows individuals to discern right from wrong and make judgments grounded in ethical considerations. as the integration of artificial intelligence (ai) systems into our daily lives deepens, the imperative for these systems to comprehend and reason about moral dilemmas becomes increasingly pronounced. the challenge lies not just in teaching machines to mimic human moral judgments but in ensuring that these judgments are grounded in a robust understanding of ethical principles. this paper aims to bridge a significant gap in the field: the absence of moral reasoning datasets for low-resource languages such as filipino. specifically, we introduce a parallel corpus for commonsense moralitydetermined heavily by emotions-available in both filipino and english and analyze its validity by using it in downstream tasks, namely text classification and text generation. we call this corpus the emotion-based morality in tagalog and english scenarios corpus or emotes-3k. the following are the contributions of the researchers: ## conclusion this research introduced the corpus: emotionbased morality in tagalog and english scenarios or emotes-3k, providing a moral reasoning dataset for the filipino language. the dataset was validated through text classification and text generation tasks. notably, the roberta model achieved an accuracy of 94.95% for english and 88.53% for filipino in moral text classification. furthermore, the finetuned flan-t5 models showcased impressive text generation capabilities, with most rouge metrics surpassing 0.70. however, the models exhibited challenges in discerning complex moral scenarios, especially those with conflicting intents. this study not only fills a gap in moral reasoning datasets for languages like filipino but also highlights the intricacies and challenges of teaching ai systems to reason about morality.