Analysing the Problem of Automatic Evaluation of Language Generation Systems

  1. Martínez-Murillo, Iván
  2. Moreda, Paloma
  3. Lloret, Elena
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2024

Número: 72

Páginas: 123-136

Tipo: Artículo

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Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

Las métricas automáticas de evaluación de texto se utilizan ampliamente para medir el rendimiento de un sistema de Generación de Lenguaje Natural (GLN). Sin embargo, estas métricas tienen varias limitaciones. Este articulo propone un estudio empírico donde se analiza el problema que tienen las métricas de evaluación actuales, como la falta capacidad que tienen estos sistemas de medir la calidad semántica de un texto, o la alta dependencia que tienen estas métricas sobre los textos contra los que se comparan. Además, se comparan sistemas de GLN tradicionales contra sistemas más actuales basados en redes neuronales. Finalmente, se propone una experimentación con GPT-4 para determinar si es una fuente fiable para evaluar la calidad de un texto. A partir de los resultados obtenidos, se puede concluir que con las métricas automáticas actuales la mejora de los sistemas neuronales frente a los tradicionales no es tan significativa. En cambio, si se analizan los aspectos cualitativos de los textos generados, si que se refleja esa mejora.

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Fuente de los datos: Dialnet