xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages (2023.acl-short)
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| Challenge: | xsim++ provides a reliable proxy for bitext mining without expensive pipelines. |
| Approach: | They propose a new proxy proxy based on similarity in a multilingual embedding space . they validate this proxy by running a significant number of bitext mining experiments for a set of low-resource languages and then train NMT systems on the mined data. |
| Outcome: | The proposed proxy improves on xsim++ and trains on the mined data. |
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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. |
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| Challenge: | Parallel sentence mining is a technique used to find matching sentence pairs from a source and target language. |
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Can Synthetic Translations Improve Bitext Quality? (2022.acl-long)
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| Challenge: | Synthetic translations have been used for a wide range of NLP tasks, but it remains unclear how they differ from naturally occurring data. |
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Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning (2023.acl-long)
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Barun Patra, Saksham Singhal, Shaohan Huang, Zewen Chi, Li Dong, Furu Wei, Vishrav Chaudhary, Xia Song
| Challenge: | XY-LENT: X-Y bitext enhanced Language ENcodings achieves state-of-the-art performance over 5 cross-lingual tasks within all model size bands. |
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Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings (2020.tacl-1)
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| Challenge: | Existing methods to extract parallel sentences from unaligned text yield surprisingly good results. |
| Approach: | They propose an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text using multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. |
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