Papers with WAT
Combining Translation Memory with Neural Machine Translation (D19-52)
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| Challenge: | Existing systems that combine translation memory and statistical machine translation (MT) models are able to translate less familiar phrases and sentences without sacrificing quality. |
| Approach: | They propose to combine translation memory and Neural Machine Translation (NMT) models to select final translation outputs when similarity score of a test source sentence exceeds the predefined threshold. |
| Outcome: | The proposed system significantly improves translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. |
Supervised neural machine translation based on data augmentation and improved training & inference process (D19-52)
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| Challenge: | This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019 . |
| Approach: | They propose a model for translation tasks of WAT 2019 that employs a Transformer model as the baseline and a deep layer model to improve translation quality. |
| Outcome: | The proposed methods can improve translation quality over traditional statistical machine translation (SMT) The proposed models can improve the translation quality of Japanese-English and Japanese-Chinese corpus. |
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web (2021.acl-long)
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| Challenge: | Using a curated common crawl corpus, we were able to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English. |
| Approach: | They use 32 snapshots of a curated common crawl corpus totaling 71 billion unique sentences to mine 10.8 billion parallel sentences out of which only 2.9 billions are aligned with English. |
| Outcome: | The proposed system outperforms the best single systems on the WMT’19 test set for English-German/Russian/Chinese and outperformed the best submission at the 2020 WAT workshop. |
From Word to World: Evaluate and Mitigate Culture Bias in LLMs via Word Association Test (2025.emnlp-main)
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| Challenge: | Multilingual and cross-cultural WAT reveal how culture modulates perceptual and interactive patterns. |
| Approach: | They propose to embed cultural-specific semantic associations directly within large language models (LLMs) to address cultural preference. |
| Outcome: | The proposed model significantly improves cross-cultural alignment, capturing diverse semantic associations. |