Evaluating the performance of an encoder-decoder model involves assessing its ability to accurately generate target sequences based on input sequences. There are several common approaches to evaluate the performance of such models:

  1. Bleu Score: The Bleu (Bilingual Evaluation Understudy) score is a widely used metric for evaluating the quality of machine-generated text. It measures the similarity between the machine-generated output and one or more reference (human-generated) sentences. A higher Bleu score indicates better performance.

  2. ROUGE Score: The ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score is another evaluation metric specifically designed for text summarization. It calculates the overlap of n-grams (contiguous sequences of n words) between the machine-generated summary and one or more reference summaries.

  3. Perplexity: Perplexity is a common metric used to evaluate the quality of language models. It measures how well a language model predicts a sample of text. Lower perplexity indicates better performance. By comparing the perplexity of a model on a test set against a baseline or other models, we can assess the relative performance.

  4. Human Evaluation: It is essential to involve human evaluation to assess the quality of the generated output. Experts or crowd-sourced evaluators can rate the generated outputs based on criteria such as fluency, coherence, and relevance to the input. This subjective evaluation provides valuable insights into the model's performance from a human perspective.

  It is important to note that no single metric can perfectly capture the performance of an encoder-decoder model. Therefore, it is recommended to employ a combination of quantitative metrics (such as Bleu, ROUGE, and perplexity) along with human evaluation to obtain a comprehensive assessment of the model's performance.