BERTScore is an evaluation metric for natural language processing (NLP) tasks that measures the similarity between a model-generated text and a reference text. Unlike traditional metrics like BLEU or ROUGE, which rely on exact word matches, BERTScore uses contextual embeddings from transformer-based models like BERT to capture semantic similarity, making it more robust for evaluating nuanced text generation.
How It Works:
- Token Embeddings: Both the generated and reference texts are converted into word embeddings using a pre-trained model like BERT.
- Similarity Calculation: The embeddings are compared using cosine similarity to determine how closely the two texts align in meaning.
- Precision, Recall, and F1: BERTScore calculates precision (how much of the generated text aligns with the reference), recall (how much of the reference is captured by the generated text), and their harmonic mean (F1 score).
Why It Matters:
BERTScore is particularly effective for tasks where meaning matters more than exact word overlap, such as machine translation, text summarization, and creative text generation. It captures subtle differences in semantics, making it a preferred metric when traditional word-based methods fall short.
This approach enables more accurate evaluations of AI-generated text, aligning assessments more closely with human judgments of quality.
