Challenge: Currently, there is no widely accepted standard for evaluation of machine translation (MT) for Chinese-to-English translation, there are no standard for standardized training sets, development sets, and test sets.
Approach: They propose to use Chinese-to-English machine translation as a benchmark . they build a highly competitive state-of-the-art MT system that outperforms reported results .
Outcome: The proposed system outperforms reported results on NIST OpenMT test sets in almost all papers published in major conferences and journals in computational linguistics and artificial intelligence in the past 11 years.

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Revisiting Machine Translation for Cross-lingual Classification (2023.emnlp-main)

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Challenge: Recent work in cross-lingual learning has pivoted around multilingual models, which are typically pretrained on unlabeled corpora in multiple languages using some form of language modeling objective.
Approach: They propose to use a stronger machine translation system to mitigat mismatch between training on original text and running inference on machine translated text.
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A Benchmark for Translations Across Styles and Language Variants (2025.findings-emnlp)

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Challenge: lack of comprehensive evaluation benchmarks has hindered progress in this field . lack of evaluation benchmarking has hinder MT's ability to generate accurate outputs .
Approach: They evaluate translations across semantic preservation, cultural and regional specificity, expression style, and fluency at both the word and sentence levels.
Outcome: The proposed evaluation framework is validated on translations of state-of-the-art large language models .
Benchmarking the Performance of Machine Translation Evaluation Metrics with Chinese Multiword Expressions (2024.lrec-main)

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Challenge: Multiword Expressions (MWEs) are hard nuts for many natural language processing tasks.
Approach: They annotate 28 types of Chinese MWEs and then examine 31 MTE metrics on groups of sentences containing different MWE.
Outcome: The results show that MT systems and MTE metrics still suffer from MWEs .
Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation (D18-1)

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Challenge: Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese–English news translation task.
Approach: They empirically test neural machine translation on a Chinese–English news translation task . they show human raters prefer human over machine translation when evaluating documents .
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Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort (2021.acl-long)

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Challenge: Existing methods to evaluate multiple systems are expensive and require human evaluators.
Approach: They propose a novel online learning approach that dynamically converges to the top-3 ranked systems for the language pairs considered by taking advantage of human feedback.
Outcome: The proposed approach converges to the top-3 ranked systems for the language pairs considered despite the lack of human feedback for many translations.
Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems (2025.findings-emnlp)

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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.
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers (2021.acl-long)

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Challenge: a meta-evaluation of machine translation (MT) has been conducted in 769 research papers . a recent study shows that evaluation practices have changed over the past decade .
Approach: They propose a meta-evaluation method for machine translation that uses BLEU scores to evaluate MT performance.
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Evaluating Automatic Metrics with Incremental Machine Translation Systems (2024.findings-emnlp)

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Challenge: Existing studies have shown that neural metrics are more reliable than non-neural metrics.
Approach: They propose to use commercial machine translations to evaluate machine translation metrics based on their preference for more recent outputs.
Outcome: The proposed dataset confirms several previous findings, including the advantage of neural metrics over non-neural ones, and also explores the debated issue of how MT quality affects metric reliability.
Has Machine Translation Evaluation Achieved Human Parity? The Human Reference and the Limits of Progress (2025.acl-short)

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Challenge: In machine translation evaluation, metric performance is assessed based on agreement with human judgments.
Approach: They incorporate human baselines into the MT meta-evaluation to gain a clearer understanding of metric performance and establish an upper bound.
Outcome: The results suggest human parity, but there are several reasons to caution .
Languages Still Left Behind: Toward a Better Multilingual Machine Translation Benchmark (2025.emnlp-main)

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Challenge: Multilingual machine translation (MT) benchmarks are widely used to evaluate the capabilities of modern MT systems.
Approach: They propose to use a multilingual machine translation benchmark to assess the capabilities of modern machine translation systems.
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