# Examining the Tip of the Iceberg: A Data Set for Idiom Translation

arXiv: https://arxiv.org/abs/1802.04681 · HTML: https://mlsanity.com/paper/1802.04681/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2018.

## Built on by (most cited first, 30 of 34)

- [Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation](https://mlsanity.com/paper/2205.15301/) (2022)
- [Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models](https://mlsanity.com/paper/2308.13961/) (2023)
- [The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities](https://mlsanity.com/paper/2405.20089/) (2024)
- [Idiomatic Expression Identification using Semantic Compatibility](https://mlsanity.com/paper/2110.10064/) (2021)
- [Improving LLM Abilities in Idiomatic Translation](https://mlsanity.com/paper/2407.03518/) (2024)
- [Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting](https://mlsanity.com/paper/2310.07081/) (2023)
- [Rolling the DICE on Idiomaticity: How LLMs Fail to Grasp Context](https://mlsanity.com/paper/2410.16069/) (2024)
- [PETCI: A Parallel English Translation Dataset of Chinese Idioms](https://mlsanity.com/paper/2202.09509/) (2022)
- [Automatic Evaluation and Analysis of Idioms in Neural Machine Translation](https://mlsanity.com/paper/2210.04545/) (2022)
- [varepsilon K'U <MASK>: Integrating Yor`ub'a cultural greetings into machine translation](https://mlsanity.com/paper/2303.17972/) (2023)
- [Idiomatic Expression Paraphrasing without Strong Supervision](https://mlsanity.com/paper/2112.08592/) (2021)
- [From Solving a Problem Boldly to Cutting the Gordian Knot: Idiomatic Text Generation](https://mlsanity.com/paper/2104.06541/) (2021)
- [Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressions](https://mlsanity.com/paper/2207.03679/) (2022)
- [The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation](https://mlsanity.com/paper/2205.06618/) (2022)
- [That was the last straw, we need more: Are Translation Systems Sensitive to Disambiguating Context?](https://mlsanity.com/paper/2310.14610/) (2023)
- [Preferences for Idiomatic Language are Acquired Slowly -- and Forgotten Quickly: A Case Study on Swedish](https://mlsanity.com/paper/2602.03484/) (2026)
- [Multi-Target Cross-Lingual Summarization: a novel task and a language-neutral approach](https://mlsanity.com/paper/2410.00502/) (2024)
- [Evaluating LLMs on Chinese Idiom Translation](https://mlsanity.com/paper/2508.10421/) (2025)
- [A Survey of Idiom Datasets for Psycholinguistic and Computational Research](https://mlsanity.com/paper/2508.11828/) (2025)
- [Round-Trip Translation Reveals What Frontier Multilingual Benchmarks Miss](https://mlsanity.com/paper/2604.12911/) (2026)
- [Generating Continuations in Multilingual Idiomatic Contexts](https://mlsanity.com/paper/2310.20195/) (2023)
- [IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions](https://mlsanity.com/paper/2312.06053/) (2023)
- [The Comparison of Translationese in Machine Translation and Human Transation in terms of Translation Relations](https://mlsanity.com/paper/2404.08661/) (2024)
- [Killing Two Flies with One Stone: An Attempt to Break LLMs Using English->Icelandic Idioms and Proper Names](https://mlsanity.com/paper/2410.03394/) (2024)
- [CLIX: Cross-Lingual Explanations of Idiomatic Expressions](https://mlsanity.com/paper/2501.03191/) (2025)
- [SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech](https://mlsanity.com/paper/2602.09866/) (2026)
- [Hubness, Not Anisotropy, Drives Cross-Lingual Retrieval Asymmetry in Multilingual Embedding Models](https://mlsanity.com/paper/2605.26575/) (2026)
- [Sentiment-based Candidate Selection for NMT](https://mlsanity.com/paper/2104.04840/) (2021)
- [AMR4NLI: Interpretable and robust NLI measures from semantic graphs](https://mlsanity.com/paper/2306.00936/) (2023)
- [Prediction of Translation Techniques for the Translation Process](https://mlsanity.com/paper/2403.14454/) (2024)

## Abstract (arXiv)

Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs. Although state-of-the-art NMT systems are generating progressively better translations, idiom translation remains one of the open challenges in this field. Idioms, a category of multiword expressions, are an interesting language phenomenon where the overall meaning of the expression cannot be composed from the meanings of its parts. A first important challenge is the lack of dedicated data sets for learning and evaluating idiom translation. In this paper we address this problem by creating the first large-scale data set for idiom translation. Our data set is automatically extracted from a widely used German-English translation corpus and includes, for each language direction, a targeted evaluation set where all sentences contain idioms and a regular training corpus where sentences including idioms are marked. We release this data set and use it to perform preliminary NMT experiments as the first step towards better idiom translation.