mlsanityData to 7 Oct 2026

The Multi-Range Theory of Translation Quality Measurement: MQM scoring models and Statistical Quality Control

arXiv 2405.16969 ↗First posted 27 May 2024cs.CL stat.APcited by 8

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Abstract (arXiv)

The year 2024 marks the 10th anniversary of the Multidimensional Quality Metrics (MQM) framework for analytic translation quality evaluation. The MQM error typology has been widely used by practitioners in the translation and localization industry and has served as the basis for many derivative projects. The annual Conference on Machine Translation (WMT) shared tasks on both human and automatic translation quality evaluations used the MQM error typology. The metric stands on two pillars: error typology and the scoring model. The scoring model calculates the quality score from annotation data, detailing how to convert error type and severity counts into numeric scores to determine if the content meets specifications. Previously, only the raw scoring model had been published. This April, the MQM Council published the Linear Calibrated Scoring Model, officially presented herein, along with the Non-Linear Scoring Model, which had not been published before. This paper details the latest MQM developments and presents a universal approach to translation quality measurement across three sample size ranges. It also explains why Statistical Quality Control should be used for very small sample sizes, starting from a single sentence.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 27 May 2024.

Builds on

8 of 19 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2021HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluationcited by 17
  4. 2021Measuring Uncertainty in Translation Quality Evaluation (TQE)cited by 16
  5. 2023Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learningcited by 8
  6. 2016Meta-Evaluation of Translation Evaluation Methods: a systematic up-to-date overviewcited by 5
  7. 2023Student's t-Distribution: On Measuring the Inter-Rater Reliability When the Observations are Scarcecited by 2
  8. 2023MTUncertainty: Assessing the Need for Post-editing of Machine Translation Outputs by Fine-tuning OpenAI LLMscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.

  1. 2025Advancing Dialectal Arabic to Modern Standard Arabic Machine Translationcited by 6
  2. 2025Non-Linear Scoring Model for Translation Quality Evaluationcited by 2
  3. 2026Round-Trip Translation Reveals What Frontier Multilingual Benchmarks Misscited by 1
  4. 2026MetaHOPE: A Metaphor-Oriented Evaluation Framework for Analysing MT and LLM Translation Errorscited by 1
  5. 2026Last Translation Benchmarkcited by 1
  6. 2026Can We Triage LLM Translation Errors in Classical Texts Without Human References? Source Novelty, GEMBA Scoring, and Budgeted Review through Pali-to-English Translationcited by 1
  7. 2026EviSI: An Evidence-Based Evaluation Agent for Simultaneous Interpretingcited by 0
  8. 2026Lossy Compressive Text Autoencoderscited by 0