Reference-free Evaluation Metrics for Text Generation: A Survey
It provides a comprehensive overview for researchers and practitioners in NLP, but is incremental as it synthesizes existing work without introducing new methods.
This survey investigates reference-free evaluation metrics for natural language generation systems, addressing the high cost and impracticality of creating human references, and highlights promising future research directions.
A number of automatic evaluation metrics have been proposed for natural language generation systems. The most common approach to automatic evaluation is the use of a reference-based metric that compares the model's output with gold-standard references written by humans. However, it is expensive to create such references, and for some tasks, such as response generation in dialogue, creating references is not a simple matter. Therefore, various reference-free metrics have been developed in recent years. In this survey, which intends to cover the full breadth of all NLG tasks, we investigate the most commonly used approaches, their application, and their other uses beyond evaluating models. The survey concludes by highlighting some promising directions for future research.