# Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation

arXiv: https://arxiv.org/abs/2406.11580 · HTML: https://mlsanity.com/paper/2406.11580/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation](https://mlsanity.com/paper/2107.10821/) (2021)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MT](https://mlsanity.com/paper/1802.06041/) (2018)
- [Finding Replicable Human Evaluations via Stable Ranking Probability](https://mlsanity.com/paper/2404.01474/) (2024)
- [Correct Me If You Can: Learning from Error Corrections and Markings](https://mlsanity.com/paper/2004.11222/) (2020)
- [Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian](https://mlsanity.com/paper/1802.01451/) (2018)
- [Searching for a higher power in the human evaluation of MT](https://mlsanity.com/paper/2210.11612/) (2022)

## Built on by (most cited first, 30 of 62)

- [How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMs](https://mlsanity.com/paper/2410.18697/) (2024)
- [How to Select Datapoints for Efficient Human Evaluation of NLG Models?](https://mlsanity.com/paper/2501.18251/) (2025)
- [AI-Assisted Human Evaluation of Machine Translation](https://mlsanity.com/paper/2406.12419/) (2024)
- [Preliminary WMT24 Ranking of General MT Systems and LLMs](https://mlsanity.com/paper/2407.19884/) (2024)
- [Scaling Low-Resource MT via Synthetic Data Generation with LLMs](https://mlsanity.com/paper/2505.14423/) (2025)
- [SeqPO-SiMT: Sequential Policy Optimization for Simultaneous Machine Translation](https://mlsanity.com/paper/2505.20622/) (2025)
- [AFRIDOC-MT: Document-level MT Corpus for African Languages](https://mlsanity.com/paper/2501.06374/) (2025)
- [Pitfalls and Outlooks in Using COMET](https://mlsanity.com/paper/2408.15366/) (2024)
- [Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics](https://mlsanity.com/paper/2410.05183/) (2024)
- [LiTransProQA: an LLM-based Literary Translation evaluation metric with Professional Question Answering](https://mlsanity.com/paper/2505.05423/) (2025)
- [Has Machine Translation Evaluation Achieved Human Parity? The Human Reference and the Limits of Progress](https://mlsanity.com/paper/2506.19571/) (2025)
- [MetricX-25 and GemSpanEval: Google Translate Submissions to the WMT25 Evaluation Shared Task](https://mlsanity.com/paper/2510.24707/) (2025)
- [Multilingual Performance Biases of Large Language Models in Education](https://mlsanity.com/paper/2504.17720/) (2025)
- [Enhancing Human Evaluation in Machine Translation with Comparative Judgment](https://mlsanity.com/paper/2502.17797/) (2025)
- [LLMs as Span Annotators: A Comparative Study of LLMs and Humans](https://mlsanity.com/paper/2504.08697/) (2025)
- [Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs](https://mlsanity.com/paper/2512.16378/) (2025)
- [QE4PE: Word-level Quality Estimation for Human Post-Editing](https://mlsanity.com/paper/2503.03044/) (2025)
- [Estimating Machine Translation Difficulty](https://mlsanity.com/paper/2508.10175/) (2025)
- [Localizing and Mitigating Errors in Long-form Question Answering](https://mlsanity.com/paper/2407.11930/) (2024)
- [OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMs](https://mlsanity.com/paper/2503.11858/) (2025)
- [Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement](https://mlsanity.com/paper/2505.23183/) (2025)
- [EuroGEST: Investigating gender stereotypes in multilingual language models](https://mlsanity.com/paper/2506.03867/) (2025)
- [Specification-Aware Machine Translation and Evaluation for Purpose Alignment](https://mlsanity.com/paper/2509.17559/) (2025)
- [How to Evaluate Speech Translation with Source-Aware Neural MT Metrics](https://mlsanity.com/paper/2511.03295/) (2025)
- [LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation](https://mlsanity.com/paper/2604.18490/) (2026)
- [Early-Exit and Instant Confidence Translation Quality Estimation](https://mlsanity.com/paper/2502.14429/) (2025)
- [How Important is `Perfect' English for Machine Translation Prompts?](https://mlsanity.com/paper/2507.09509/) (2025)
- [COMET-poly: Machine Translation Metric Grounded in Other Candidates](https://mlsanity.com/paper/2508.18549/) (2025)
- [Long-context Reference-based MT Quality Estimation](https://mlsanity.com/paper/2509.13980/) (2025)
- [MQM Re-Annotation: A Technique for Collaborative Evaluation of Machine Translation](https://mlsanity.com/paper/2510.24664/) (2025)

## Abstract (arXiv)

High-quality Machine Translation (MT) evaluation relies heavily on human judgments. Comprehensive error classification methods, such as Multidimensional Quality Metrics (MQM), are expensive as they are time-consuming and can only be done by experts, whose availability may be limited especially for low-resource languages. On the other hand, just assigning overall scores, like Direct Assessment (DA), is simpler and faster and can be done by translators of any level, but is less reliable. In this paper, we introduce Error Span Annotation (ESA), a human evaluation protocol which combines the continuous rating of DA with the high-level error severity span marking of MQM. We validate ESA by comparing it to MQM and DA for 12 MT systems and one human reference translation (English to German) from WMT23. The results show that ESA offers faster and cheaper annotations than MQM at the same quality level, without the requirement of expensive MQM experts.