| Challenge: | Existing approaches to automate the complex task of translation are tedious and expensive. |
| Approach: | They describe acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. |
| Outcome: | The proposed corpus outperforms statistical machine translation models by six to seven BLEU points . the results show that the subword models outperformed word-based models by three to four BLUE points compared with the word-base models . |
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| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
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Parallel Corpora for bi-lingual English-Ethiopian Languages Statistical Machine Translation (C18-1)
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Solomon Teferra Abate, Michael Melese, Martha Yifiru Tachbelie, Million Meshesha, Solomon Atinafu, Wondwossen Mulugeta, Yaregal Assabie, Hafte Abera, Binyam Ephrem, Tewodros Abebe, Wondimagegnhue Tsegaye, Amanuel Lemma, Tsegaye Andargie, Seifedin Shifaw
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An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages (2020.lrec-1)
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| Challenge: | In this study, we explore massively multilingual low-resource neural machine translation. |
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Improving Machine Translation with Phrase Pair Injection and Corpus Filtering (2022.emnlp-main)
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| Challenge: | In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation systems. |
| Approach: | They propose to combine Phrase Pair Injection and Corpus Filtering to boost performance of Neural Machine Translation systems. |
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AFRIDOC-MT: Document-level MT Corpus for African Languages (2025.emnlp-main)
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Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, Dietrich Klakow
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Esposito: An English-Persian Scientific Parallel Corpus for Machine Translation (2024.lrec-main)
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Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languages (L18-1)
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| Challenge: | In machine translation, we often try to collect resources to improve performance. |
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Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation (2020.emnlp-main)
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Tahmid Hasan, Abhik Bhattacharjee, Kazi Samin, Masum Hasan, Madhusudan Basak, M. Sohel Rahman, Rifat Shahriyar
| Challenge: | despite being the seventh most widely spoken language, Bengali has received little attention in machine translation due to being low in resources. |
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Low-resource Neural Machine Translation with Cross-modal Alignment (2022.emnlp-main)
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| Challenge: | Existing neural machine translation techniques rely on large monolingual corpus, which is costly for some low-resource languages. |
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