mlsanityData to 7 Oct 2026

MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language

arXiv 2406.13698 ↗First posted 19 Jun 2024cs.CLcited by 10

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Abstract (arXiv)

Machine Translation (MT) has developed rapidly since the release of Large Language Models and current MT evaluation is performed through comparison with reference human translations or by predicting quality scores from human-labeled data. However, these mainstream evaluation methods mainly focus on fluency and factual reliability, whilst paying little attention to figurative quality. In this paper, we investigate the figurative quality of MT and propose a set of human evaluation metrics focused on the translation of figurative language. We additionally present a multilingual parallel metaphor corpus generated by post-editing. Our evaluation protocol is designed to estimate four aspects of MT: Metaphorical Equivalence, Emotion, Authenticity, and Quality. In doing so, we observe that translations of figurative expressions display different traits from literal ones.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 17 of its 74 references that are in the map. First posted on arXiv 19 Jun 2024.

Builds on

17 of 74 references.

References · 17

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  2. 2023Large Language Models Are State-of-the-Art Evaluators of Translation Qualitycited by 416
  3. 2023Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Modelscited by 87
  4. 2020TransQuest: Translation Quality Estimation with Cross-lingual Transformerscited by 59
  5. 2021MERMAID: Metaphor Generation with Symbolism and Discriminative Decodingcited by 49
  6. 2021Metaphor Generation with Conceptual Mappingscited by 32
  7. 2022Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languagescited by 30
  8. 2020Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representationscited by 21
  9. 2023FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learningcited by 18
  10. 2023Metaphor Detection with Effective Context Denoisingcited by 13
  11. 2022As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioningcited by 11
  12. 2023The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generationcited by 11
5 more
  1. 2024CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generationcited by 11
  2. 2024From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimationcited by 10
  3. 2020"Love is as Complex as Math": Metaphor Generation System for Social Chatbotcited by 9
  4. 2023The Secret of Metaphor on Expressing Stronger Emotioncited by 9
  5. 2020Zero-Shot Translation Quality Estimation with Explicit Cross-Lingual Patternscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 7.

10 papers.

  1. 2024Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generationcited by 2
  2. 2025Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings for CLEF JOKER 2025 Task 2cited by 1
  3. 2026MetaHOPE: A Metaphor-Oriented Evaluation Framework for Analysing MT and LLM Translation Errorscited by 1
  4. 2026Last Translation Benchmarkcited by 1
  5. 2025Natural Language Generationcited by 0
  6. 2026Towards High-Level Semantic Intelligencecited by 0
  7. 2026Towards End-to-End Multilingual Metaphor Processing: Integrating Detection, Translation, and Evaluationcited by 0
  8. 2026Searching for Sound-Meaning Collisions: Graph-Based Affordance Retrieval and Multi-Evaluator Ranking for Pun Translation at CLEF 2026 JOKER Task 2cited by 0
  9. 2026Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddingscited by 0
  10. 2026AraMIP: Extending MIPVU Towards Metaphor Identification in Arabiccited by 0