# A new approach to calculating BERTScore for automatic assessment of translation quality

arXiv: https://arxiv.org/abs/2203.05598 · HTML: https://mlsanity.com/paper/2203.05598/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing](https://mlsanity.com/paper/1902.09492/) (2019)
- [Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing](https://mlsanity.com/paper/1909.06775/) (2019)
- [Translation Quality Assessment: A Brief Survey on Manual and Automatic Methods](https://mlsanity.com/paper/2105.03311/) (2021)
- [Quality expectations of machine translation](https://mlsanity.com/paper/1803.08409/) (2018)
- [Zero-Shot Translation Quality Estimation with Explicit Cross-Lingual Patterns](https://mlsanity.com/paper/2010.04989/) (2020)

## Built on by (most cited first, 1 of 1)

- [Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization](https://mlsanity.com/paper/2507.08342/) (2025)

## Abstract (arXiv)

The study of the applicability of the BERTScore metric was conducted to translation quality assessment at the sentence level for English -> Russian direction. Experiments were performed with a pre-trained Multilingual BERT as well as with a pair of Monolingual BERT models. To align monolingual embeddings, an orthogonal transformation based on anchor tokens was used. It was demonstrated that such transformation helps to prevent mismatching issue and shown that this approach gives better results than using embeddings of the Multilingual model. To improve the token matching process it is proposed to combine all incomplete WorkPiece tokens into meaningful words and use simple averaging of corresponding vectors and to calculate BERTScore based on anchor tokens only. Such modifications allowed us to achieve a better correlation of the model predictions with human judgments. In addition to evaluating machine translation, several versions of human translation were evaluated as well, the problems of this approach were listed.