Papers with Translation quality
Almost Free Semantic Draft for Neural Machine Translation (2021.naacl-main)
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| Challenge: | Empirical experiments show that the presented method can achieve competitive performance in common language pairs with a clear advantage in inference efficiency. |
| Approach: | They propose a method to sample and consider a semantic draft as global information from semantic space for decoding with almost free of cost. |
| Outcome: | Empirical results show that the proposed method can achieve competitive performance in common language pairs with a clear advantage in inference efficiency. |
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 . |
| Outcome: | The proposed model performs better in both directions than other models. |
CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation (2023.findings-emnlp)
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| Challenge: | Large language models have demonstrated the capability to perform on machine translation when the input is prompted with a few examples. |
| Approach: | They propose a regression model that combine features influencing example selection to maximize translation quality. |
| Outcome: | The proposed model outperforms random selection and strong single-factor baselines on multiple language pairs and language models. |