# Machine Translation Evaluation with BERT Regressor

arXiv: https://arxiv.org/abs/1907.12679 · HTML: https://mlsanity.com/paper/1907.12679/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Ditch the Gold Standard: Re-evaluating Conversational Question Answering](https://mlsanity.com/paper/2112.08812/) (2021)
- [Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach](https://mlsanity.com/paper/2102.10242/) (2021)
- [Naturalness Evaluation of Natural Language Generation in Task-oriented Dialogues using BERT](https://mlsanity.com/paper/2109.02938/) (2021)
- [BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment](https://mlsanity.com/paper/2201.10243/) (2022)
- [Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review](https://mlsanity.com/paper/2409.18170/) (2024)
- [SummScore: A Comprehensive Evaluation Metric for Summary Quality Based on Cross-Encoder](https://mlsanity.com/paper/2207.04660/) (2022)

## Abstract (arXiv)

We introduce the metric using BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019) for automatic machine translation evaluation. The experimental results of the WMT-2017 Metrics Shared Task dataset show that our metric achieves state-of-the-art performance in segment-level metrics task for all to-English language pairs.