Challenge: idiom translation is a challenging problem in machine translation because meaning is non-compositional and literal translations are likely to be wrong.
Approach: They propose a method to evaluate the quality of idiom translation of MT systems by a blacklist of literal translations.
Outcome: The proposed method detects that a sizable number of idioms are mistranslated (46.1%) and that literal translation error is a common error type.

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Automatic Evaluation and Analysis of Idioms in Neural Machine Translation (2023.eacl-main)

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Challenge: Neural machine translation (NMT) struggles with the translation of rare multi-word expressions (MWEs).
Approach: They propose a metric for automatically measuring the frequency of literal translation errors without human involvement.
Outcome: The proposed metric measures the frequency of literal translation errors without human involvement with the models trained in different conditions and across a wide range of metrics and test sets.
Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation (L18-1)

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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.
Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting (2023.emnlp-main)

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Challenge: idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts.
Approach: They propose to use retrieval-augmented models to increase the accuracy of a strong pretrained machine translation model on idiomatic sentences by up to 13%.
Outcome: The proposed techniques improve the accuracy of a strong pretrained model on idiomatic sentences by up to 13% in absolute accuracy, and holds potential benefits for non-idiomatic phrases.
It’s Not a Walk in the Park! Challenges of Idiom Translation in Speech-to-text Systems (2025.acl-long)

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Challenge: idioms are defined as words with a figurative meaning not deducible from their individual components.
Approach: They compare idiom translation as compared to conventional news translation in two languages . they compare MT and SLT systems with MT, Large Language Models and cascaded alternatives .
Outcome: The proposed systems show better handling of idioms than standard news translation systems.
Examining the Tip of the Iceberg: A Data Set for Idiom Translation (L18-1)

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Challenge: Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs.
Approach: They propose to use a large-scale data set to evaluate idiom translation in GermanEnglish.
Outcome: The proposed dataset is used to perform preliminary NMT experiments on idiom translation in GermanEnglish.
CHENGYU-BENCH: Benchmarking Large Language Models for Chinese Idiom Understanding and Use (2025.emnlp-main)

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Challenge: Existing benchmarks focus on narrow tasks such as multiple-choice cloze tests, isolated translation, or simple paraphrasing.
Approach: They propose a benchmark to measure Chinese idioms' cultural and contextual nuances . they evaluate 2,937 human-verified examples covering 1,765 common idiomes .
Outcome: The proposed benchmarks achieve 95% accuracy on Evaluative Connotation, but only 85% on Appropriateness and 40% top-1 accuracy in Open Cloze.
Large Language Models for Persian-English Idiom Translation (2025.naacl-long)

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Challenge: Large language models have shown superior capabilities in translating figurative language compared to neural machine translation systems.
Approach: They evaluate LLMs, NMTs and their combinations using PersianIdioms datasets . they find that automatic evaluation methods like BLEU and BERTScore are effective .
Outcome: The proposed model performs better in both directions than other models.
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.
Informative Manual Evaluation of Machine Translation Output (2020.coling-main)

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Challenge: a new method for manual evaluation of machine translation output is proposed . evaluators mark problematic parts of the translated text, not just overall scores .
Approach: They propose a method for manual evaluation of machine translation output based on marking actual issues in the translated text.
Outcome: The proposed method can be applied on any genre/domain and language pair . it can be guided by various types of quality criteria and can be used for other types of generated text.
Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation (2022.acl-long)

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Challenge: Unlike literal expressions, idioms’ meanings do not follow from their parts, posing a challenge for neural machine translation (NMT).
Approach: They examine the mechanics of the dominant NMT model, Transformer, and their effect on their understanding of idioms.
Outcome: The proposed model over-generates compositional, literal translations and is unable to translate idioms accurately.

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