| 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. |
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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. |
Analyzing Challenges in Neural Machine Translation for Software Localization (2023.eacl-main)
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| Challenge: | Neural machine translation (NMT) is a new form of machine translation that reduces the post-editing time of human annotators. |
| Approach: | They propose to use a novel multilingual UI corpus collection to test NMT for user interfaces. |
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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%. |
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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 . |
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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. |
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Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena (2021.acl-srw)
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| Challenge: | Using a semi-automatic process, we observe the linguistic performance of various neural machine translation models. |
| Approach: | They observe the linguistic performance of a neural machine translation model on several steps on the training process. |
| Outcome: | The proposed system performs well on training of English-to-German models. |
Better Neural Machine Translation by Extracting Linguistic Information from BERT (2021.eacl-main)
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| Challenge: | Experimental results show that incorporating linguistic information into neural machine translation models is no more difficult to train than conventional Transformer-based NMT. |
| Approach: | They propose to extract linguistic information from contextual word embeddings instead of point estimates to augment NMT models. |
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No more beating about the bush : A Step towards Idiom Handling for Indian Language NLP (L18-1)
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| Challenge: | idioms are a part of natural language and are difficult to learn with a parallel corpora database. |
| Approach: | They propose to use a parallel idiom dataset to train two NLP subtasks . they show significant improvement in the two subtask training without the idiomatic dataset . |
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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 . |
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LIdioms: A Multilingual Linked Idioms Data Set (L18-1)
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| Challenge: | Recent studies have focused on linguistic data sets that are bilingual on the Linguistic Linked Open Data (LLOD) 1 . |
| Approach: | They describe a multilingual RDF representation of idioms currently containing five languages . they use a model to structure the data and a method to link the data to well-known multilingual data sets such as BabelNet. |
| Outcome: | The proposed model complies with best practices according to Linguistic Linked Open Data Community. |