What does BERTScore measure in text generation models?

BERTScore measures the similarity between generated text and reference text by utilizing BERT embeddings to quantify semantic similarity at the token level. In practice, BERTScore compares the word embeddings of the generated text with those of reference text, effectively recognizing and scoring the nuances of meaning that may be present even if the exact word choices differ. This approach allows for a more semantic understanding of how well a text generation model produces content that aligns with human-like understanding compared to traditional metrics, which might focus solely on surface-level word matching.

Utilizing BERT embeddings helps capture contextual information and relationships between words, which enhances the evaluation of text generated by models based on its semantic relevance rather than just syntactic similarity. Because of this capability, BERTScore is particularly effective in tasks where understanding sentiment, context, or nuanced meaning is critical in comparing generated responses with human sources.