Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts
Isabel Groves | Ye Tian | Ioannis Douratsos
Proceedings of the 11th International Conference on Natural Language Generation
The current most popular method for automatic Natural Language Generation (NLG) evaluation is comparing generated text with human-written reference sentences using a metrics system, which has drawbacks around reliability and scalability. We draw inspiration from second language (L2) assessment and extract a set of linguistic features to predict human judgments of sentence naturalness. Our experiment using a small dataset showed that the feature-based approach yields promising results, with the added potential of providing interpretability into the source of the problems.