Papers with alignment error rate
Noisy Parallel Data Alignment (2023.findings-eacl)
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| Challenge: | Optical character recognition (OCR) is used to convert endangered language documents into machine-readable data, but its noisy outputs are a challenge for many under-resourced languages. |
| Approach: | They propose to use optical character recognition (OCR) to convert endangered language documents into machine-readable data by using noisy alignment models. |
| Outcome: | The proposed model reduces alignment error rate on a state-of-the-art neural-based alignment model up to 59.6%. |
Target Foresight Based Attention for Neural Machine Translation (N18-1)
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| Challenge: | Empirical experiments on Chinese-to-English and Japanese-to English datasets show that the proposed attention model delivers significant improvements in terms of alignment error rate and BLEU. |
| Approach: | They propose to explicitly access the target foresight word in the attention model to improve alignment and translation accuracy. |
| Outcome: | Empirical results show that the proposed model improves alignment error rate and BLEU on Chinese-to-English and Japanese-toEnglish datasets. |