|
--- |
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language_creators: |
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- expert-generated |
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language: |
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- de |
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- en |
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- ru |
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multilinguality: |
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- translation |
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- multilingual |
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license: cc-by-4.0 |
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configs: |
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- config_name: de-en |
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data_files: |
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- split: test |
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path: data/de-en.json |
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- config_name: en-de |
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data_files: |
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- split: test |
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path: data/en-de.json |
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- config_name: ru-en |
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data_files: |
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- split: test |
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path: data/ru-en.json |
|
--- |
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## IdiomsInCtx-MT Dataset |
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|
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This repository contains the IdiomsInCtx-MT dataset used in our ACL 2024 paper: [The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities]([https://arxiv.org/abs/2405.20089](https://aclanthology.org/2024.acl-long.336/)). See [this GitHub repo](https://github.com/amazon-science/idioms-incontext-mt) for the origin of the data. |
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### Description |
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The dataset consists of idiomatic expressions in context and their human-written translations. There are 1000 translations per direction. The dataset covers 2 language pairs (English-German and English-Russian) with 3 translation directions: |
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1. English → German (`en-de`) |
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2. German → English (`de-en`) |
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3. Russian → English (`ru-en`) |
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|
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The dataset is designed to evaluate the performance of large language models and machine translation systems in handling idiomatic expressions, which can be challenging due to their non-literal meanings. |
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|
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### Usage |
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|
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```python |
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>>> dataset = load_dataset("davidstap/IdiomsInCtx-MT", "de-en") # available directions: de-en, en-de, ru-en |
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>>> dataset |
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DatasetDict({ |
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test: Dataset({ |
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features: ['de', 'en'], |
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num_rows: 1000 |
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}) |
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}) |
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>>> dataset['test']['de'][0] |
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'Es ist mir wurst, wenn du nicht kommst.' |
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>>> dataset['test']['en'][0] |
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"I couldn't care less if you don't come." |
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``` |
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### Citation |
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If you use this dataset in your work, please cite our paper: |
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|
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``` |
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@inproceedings{stap-etal-2024-fine, |
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title = "The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing {LLM} Abilities", |
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author = "Stap, David and |
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Hasler, Eva and |
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Byrne, Bill and |
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Monz, Christof and |
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Tran, Ke", |
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booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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year = "2024", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.acl-long.336", |
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pages = "6189--6206", |
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} |
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``` |
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|
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### License |
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|
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This dataset is licensed under the CC-BY-NC-4.0 License. |
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