Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers
This work provides guidelines for improving evaluation practices in creative NLG, addressing a methodological gap for researchers in the field, though it is incremental as it synthesizes existing literature.
The paper surveyed human evaluation methods in creative natural language generation papers from INLG 2020 and ICCC 2020, finding that scaled surveys on a 5-point scale are most common and identifying key evaluation parameters like meaning and novelty.
We survey human evaluation in papers presenting work on creative natural language generation that have been published in INLG 2020 and ICCC 2020. The most typical human evaluation method is a scaled survey, typically on a 5 point scale, while many other less common methods exist. The most commonly evaluated parameters are meaning, syntactic correctness, novelty, relevance and emotional value, among many others. Our guidelines for future evaluation include clearly defining the goal of the generative system, asking questions as concrete as possible, testing the evaluation setup, using multiple different evaluation setups, reporting the entire evaluation process and potential biases clearly, and finally analyzing the evaluation results in a more profound way than merely reporting the most typical statistics.