An Empirical Study on the Number of Items in Human Evaluation of Automatically Generated Texts

• Autores: Javier González Corbelle, José M. Alonso Moral , Rosa M. Crujeiras Casais, Alberto José Bugarín Diz
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 72, 2024, págs. 45-55
• Idioma: inglés
• Títulos paralelos:
  • Estudio Empírico sobre el Numero de Elementos en la Evaluación Humana de Textos Generados Automáticamente
• Enlaces
  • Texto completo
• Resumen
  • español

    La evaluación humana de modelos neuronales en Generación de Lenguaje Natural (GLN) requiere un diseño experimental cuidadoso de elementos como, por ejemplo, número de evaluadores, número de ítems a evaluar, número de criterios de calidad, entre otros, para así garantizar la reproducibilidad de experimentos, así como para asegurar que las conclusiones extraídas son significativas. Aunque existen algunas recomendaciones genéricas sobre cómo proceder, no existe un protocolo de evaluación consensuado, general y aceptado. En este artículo prestamos atención a cómo influye el número de elementos a evaluar en la evaluación humana de los sistemas de GLN. Aplicamos distintos métodos de remuestreo para simular la evaluación de distintos conjuntos de ítems por parte de cada evaluador. A continuación, comparamos los resultados obtenidos evaluando sólo un conjunto limitado de ítems con los obtenidos evaluando todas las salidas del sistema para el conjunto completo de casos de prueba. Las conclusiones derivadas del estudio empírico corroboran la hipótesis de investigación de partida: el uso de técnicas de remuestreo ayuda a obtener resultados de evaluación significativos incluso con un número pequeño de ítems a evaluar por cada evaluador.

  • English

    Human evaluation of neural models in Natural Language Generation (NLG) requires a careful experimental design in terms of the number of evaluators, number of items to assess, number of quality criteria, among other factors, for the sake of reproducibility as well as for ensuring that significant conclusions are drawn. Although there are some generic recommendations on how to proceed, there is not an established or accepted evaluation protocol admitted worldwide yet. In this paper, we address empirically the impact of the number of items to assess in the context of human evaluation of NLG systems. We first apply resampling methods to simulate the evaluation of different sets of items by each evaluator. Then, we compare the results obtained by evaluating only a limited set of items with those obtained by evaluating all outputs of the system for a given test set. Empirical findings validate the research hypothesis: well-known resampling statistical methods can contribute to getting significant results even with a small number of items to be evaluated by each evaluator.

• Referencias bibliográficas
  • Altman, D. G. 1991. Practical Statistics for Medical Research. Chapman and Hall.
  • Banerjee, S. and A. Lavie. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings...
  • Belz, A. 2022. A Metrological Perspective on Reproducibility in NLP. Computational Linguistics, 48(4):1125–1135. Belz, A., C. Thomson, E....
  • Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1):37–46.
  • De Leon, A. and Y. Zhu. 2008. ANOVA extensions for mixed discrete and continuous data. Computational Statistics Data Analysis, 52(4):2218–2227.
  • Efron, B. 1979. Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1):1 – 26.
  • Faul, F., E. Erdfelder, A. Buchner, and A.-G. Lang. 2009. Statistical power analyses using G*Power 3.1: Tests for correlation and regression...
  • Fisher, R. A., 1992. Breakthroughs in Statistics: Methodology and Distribution, chapter Statistical Methods for Research Workers, pages 66–70....
  • Fleiss, J. L. 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin, 76:378–382.
  • González Corbelle, J., A. Bugarín-Diz, J. Alonso-Moral, and J. Taboada. 2022. Dealing with hallucination and omission in neural natural language...
  • Hesterberg, T. 2008. It’s time to retire the “n ≥ 30” rule. In Proceedings of the American Statistical Association, Alexandria VA.
  • Kane, H., M. Y. Kocyigit, A. Abdalla, P. Ajanoh, and M. Coulibali. 2020. NUBIA: NeUral based interchangeability assessor for text generation....
  • Lin, C.-Y. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain....
  • Mair, P. and R. Wilcox. 2020. Robust Statistical Methods in R Using the WRS2 Package. Behavior Research Methods, 52:464–488.
  • Moramarco, F., A. Papadopoulos Korfiatis, M. Perera, D. Juric, J. Flann, E. Reiter, A. Belz, and A. Savkov. 2022. Human evaluation and correlation...
  • Obeid, J. and E. Hoque. 2020. Chart-to-text: Generating natural language descriptions for charts by adapting the Transformer model. In Proceedings...
  • Papineni, K., S. Roukos, T.Ward, and W.-J. Zhu. 2002. BLEU: A method for automatic evaluation of machine translation. In Proceedings of the...
  • Reiter, E. 2018. A structured review of the validity of BLEU. Computational Linguistics, 44(3):393–401.
  • Sellam, T., D. Das, and A. Parikh. 2020. BLEURT: Learning robust metrics for text generation. In Proceedings of the 58th Annual Meeting of...
  • Student. 1908. Probable error of a correlation coefficient. Biometrika, 6(2/3):302–310.
  • Van der Lee, C., A. Gatt, E. van Miltenburg, and E. Krahmer. 2021. Human evaluation of automatically generated text: Current trends and best...
  • Wang, J., Y. Liang, F. Meng, Z. Sun, H. Shi, Z. Li, J. Xu, J. Qu, and J. Zhou. 2023. Is ChatGPT a good NLG evaluator? a preliminary study....
  • Zhang, T., V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi. 2020. BERTScore: Evaluating text generation with BERT. In International Conference...