MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment. |
| Approach: | They propose a framework that automatically post-edits the original translation based on each error, thereby filtering out non-impactful errors. |
| Outcome: | The proposed framework improves reliability and quality of error spans against GEMBA-MQM, across eight LLMs in both high- and low-resource languages. |
Similar Papers
Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations (2024.findings-naacl)
Copied to clipboard
| Challenge: | supervised systems have not replaced dedicated supervised models for machine translation tasks. |
| Approach: | They propose to guide LLMs to post-edit MT with feedback from MQM annotations . they then fine-tune the LLM to improve its ability to exploit the feedback . |
| Outcome: | The proposed model improves TER, BLEU and COMET scores on Chinese-English, English-German and English-Russian data. |
Diagnose, Then Repair: A Two-Stage MQM-Guided Post-Editing Framework for Domain-Specific Machine Translation (2026.acl-industry)
Copied to clipboard
| Challenge: | In practice, LLMs are largely diagnostic, with the signals rarely translating into direct quality improvements under real production constraints. |
| Approach: | They propose a two-stage, evaluator-guided automatic post-editing framework that turns MQM-style evaluation into targeted repairs. |
| Outcome: | The proposed framework improves both COMET and CometKiwi scores over one-stage evaluation methods while severities and error spans show strong agreement with human annotations and human editor preferences. |
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset (2022.lrec-1)
Copied to clipboard
Marina Fomicheva, Shuo Sun, Erick Fonseca, Chrysoula Zerva, Frédéric Blain, Vishrav Chaudhary, Francisco Guzmán, Nina Lopatina, Lucia Specia, André F. T. Martins
| Challenge: | Existing datasets for machine translation quality estimation and post-editing have several shortcomings. |
| Approach: | They propose a dataset for machine translation quality estimation and automatic post-editing . they report the performance of baseline systems trained on the MLQE-PE dataset . |
| Outcome: | The proposed dataset contains human labels for up to 10,000 translations per language pair. |
MQM Re-Annotation: A Technique for Collaborative Evaluation of Machine Translation (2026.acl-long)
Copied to clipboard
| Challenge: | a critical component of machine translation model development is evaluating model quality. |
| Approach: | They propose a two-stage version of the current translation evaluation paradigm (MQM) they propose re-annotation, which uses raters to review and edit annotations . |
| Outcome: | The proposed method improves annotation quality by finding errors missed in the first pass. |
RUBRIC-MQM : Span-Level LLM-as-judge in Machine Translation For High-End Models (2025.acl-industry)
Copied to clipboard
| Challenge: | Existing LLMs are unable to match outputs due to their open-ended nature . |
| Approach: | They propose a meta-evaluation strategy PromptCUE to evaluate cutting-edge LAJ-MT models such as GEMBA-MQM and a rubric-style prompt tailored to the characteristics of LLMs. |
| Outcome: | The proposed model is able to predict scores or identify errors for individual sentences and is reliable in the real world. |
Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems (2025.findings-emnlp)
Copied to clipboard
Dmitry Popov, Vladislav Negodin, Ekaterina Enikeeva, Iana Matrosova, Nikolay Karpachev, Max Ryabinin
| Challenge: | Currently, traditional evaluation methods struggle to detect subtle translation errors. |
| Approach: | They propose to use a dataset of human evaluations for English–Russian translations created by professional linguists to enable consistent and rich annotation. |
| Outcome: | The proposed protocol allows expert assessments without time pressure to yield substantially different results from standard evaluations. |
Enhancing Human Evaluation in Machine Translation with Comparative Judgement (2025.acl-long)
Copied to clipboard
| Challenge: | Human evaluation is crucial for assessing rapidly evolving language models but is influenced by annotator proficiency and task design. |
| Approach: | They evaluate three annotation setups to integrate comparative judgment into human annotation for machine translation. |
| Outcome: | The proposed approach improves inter-annotator agreement and stability of the annotations. |
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models have demonstrated considerable success in various natural language processing tasks, but their performance in NMT tasks is still underexplored. |
| Approach: | They propose to use LLMs as automatic post-editors rather than direct translators to improve BLEU and COMET performance. |
| Outcome: | The proposed approach improves BLEU but COMET performance compared to in-context learning. |
LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing MT evaluation frameworks fail to capture dialect- and culture-specific errors in diglossic languages. |
| Approach: | They propose a hierarchical error taxonomy for diagnosing MT errors through six linguistic levels: sociolinguistics, pragmatics, semantics, morphosyntax, orthography, and graphetics. |
| Outcome: | The proposed framework produces 6,113 labeled error spans across 3,495 unique erroneous sentences . it is language-agnostic and can be easily applied to or adapted for other languages. |
Multi-Dimensional Machine Translation Evaluation: Model Evaluation and Resource for Korean (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies on MT evaluation characterize quality of output with a single number . a recent advancement in MT technologies has enabled higher-quality, more nuanced translations . |
| Approach: | They propose a 1200-sentence MQM evaluation benchmark for English-Korean and a reference-free QE setup to evaluate the quality of the translations. |
| Outcome: | The proposed model outperforms the existing model in style and accuracy. |