MultiSlav：利用跨语言知识迁移对抗多语言诅咒
MultiSlav: Using Cross-Lingual Knowledge Transfer to Combat the Curse of Multilinguality
AI总结：
针对斯拉夫语族NMT研究不足的问题，该研究探索跨语言知识迁移方法，证实其在低资源零样本场景下的增益，推出了该语族的最优开源NMT模型并公开发布。
摘要：
多语言神经机器翻译（NMT）会引发“多语言诅咒”，还是能在同一语系内实现跨语言知识迁移？本研究探索了多种拓展NMT可用数据范围的方法，证明即便在低资源语言的零样本翻译场景下，跨语言迁移也能带来增益。通过本文，我们推出了用于选定斯拉夫语族语言间翻译的当前最优开源NMT模型，模型已以CC BY 4.0许可证发布在HuggingFace Hub（https://hf.co/collections/allegro/multislav-6793d6b6419e5963e759a683）。斯拉夫语族包含形态丰富的中东欧语言，尽管拥有数亿母语使用者，但我们认为斯拉夫语神经机器翻译的研究仍不充分。近期多数NMT研究要么聚焦英语、西班牙语、德语等高资源语言——WMT23通用翻译任务的8个翻译方向中有7个涉及英语；要么聚焦覆盖多语族的大规模多语言模型，或是评估技术。
英文摘要：
Does multilingual Neural Machine Translation (NMT) lead to The Curse of the Multlinguality or provides the Cross-lingual Knowledge Transfer within a language family? In this study, we explore multiple approaches for extending the available data-regime in NMT and we prove cross-lingual benefits even in 0-shot translation regime for low-resource languages. With this paper, we provide state-of-the-art open-source NMT models for translating between selected Slavic languages. We released our models on the HuggingFace Hub (https://hf.co/collections/allegro/multislav-6793d6b6419e5963e759a683) under the CC BY 4.0 license. Slavic language family comprises morphologically rich Central and Eastern European languages. Although counting hundreds of millions of native speakers, Slavic Neural Machine Translation is under-studied in our opinion. Recently, most NMT research focuses either on: high-resource languages like English, Spanish, and German - in WMT23 General Translation Task 7 out of 8 task directions are from or to English; massively multilingual models covering multiple language groups; or evaluation techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
