# BERTScore: Evaluating Text Generation with BERT

arXiv: https://arxiv.org/abs/1904.09675 · HTML: https://mlsanity.com/paper/1904.09675/

## Key facts

- Cited by 5,512 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2019.

## Built on by (most cited first, 30 of 5512)

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- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
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## Abstract (arXiv)

We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sentence with each token in the reference sentence. However, instead of exact matches, we compute token similarity using contextual embeddings. We evaluate using the outputs of 363 machine translation and image captioning systems. BERTScore correlates better with human judgments and provides stronger model selection performance than existing metrics. Finally, we use an adversarial paraphrase detection task to show that BERTScore is more robust to challenging examples when compared to existing metrics.