Neural Metric

Neural metrics are automated methods for evaluating the quality of machine-translated text, aiming to better align with human judgment than traditional metrics like BLEU. Current research focuses on improving the correlation of these metrics with human assessments, addressing biases and opacity through techniques like minimum risk training and explainability methods, and exploring their application beyond machine translation to areas such as summarization and speech analysis. These advancements are significant because more accurate and reliable evaluation metrics are crucial for improving machine translation systems and other natural language processing tasks.