Detecting Non-literal Translations by Fine-tuning Cross-lingual Pre-trained Language Models (2020.coling-main)
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| Challenge: | Non-literal translations are difficult to produce even for human translators, especially for foreign language learners, and machine translations have not yet been developed to simulate human translations. |
| Approach: | They propose to fine-tune generic sentence representations produced by a pre-trained cross-lingual language model to detect non-literal translations. |
| Outcome: | The proposed model can predict human translations and distinguish literal and non-literal translations at phrase level with a moderate positive correlation. |
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