Analysing the Problem of Automatic Evaluation of Language Generation Systems

Iván Martínez-Murillo, Paloma Moreda, Elena Lloret


Automatic text evaluation metrics are widely used to measure the performance of a Natural Language Generation (NLG) system. However, these metrics have several limitations. This article empirically analyses the problem with current evaluation metrics, such as their lack of ability to measure the semantic quality of a text or their high dependence on the texts they are compared against. Additionally, traditional NLG systems are compared against more recent systems based on neural networks. Finally, an experiment with GPT-4 is proposed to determine if it is a reliable source for evaluating the validity of a text. From the results obtained, it can be concluded that with the current automatic metrics, the improvement of neural systems compared to traditional ones is not so significant. On the other hand, if we analyse the qualitative aspects of the texts generated, this improvement is reflected.

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