探究印度语言多语言机器翻译中的词汇共享
Investigating Lexical Sharing in Multilingual Machine Translation for Indian Languages
AI总结：
本研究聚焦印度语言多语言机器翻译的词汇共享，探究数据采样与词表大小的性能权衡、音译对跨文字泛化的作用及对未见语言的泛化性，发现音译无明显提升，原文字训练的模型已具备跨文字鲁棒性。
摘要：
多语言语言模型已在多种语言和任务中展现出出色的跨语言迁移能力。为提升这类模型的跨语言能力，现有策略包括音译以及将文本细粒度切分为字符而非子词。本研究探究了印地语、古吉拉特语、尼泊尔语到英语的多语言机器翻译（MT）中的词汇共享问题。我们探索了数据采样与词表大小在翻译性能间的权衡，还探究了音译是否有助于促进跨文字泛化。我们也验证了不同设置对未见过的语言（马拉地语和孟加拉语）的泛化效果。研究发现，音译并未带来显著提升，且分析表明，在原文字上训练的多语言MT模型即使对资源相对匮乏的语言，似乎也已能稳健应对跨文字差异。
英文摘要：
Multilingual language models have shown impressive cross-lingual transfer ability across a diverse set of languages and tasks. To improve the cross-lingual ability of these models, some strategies include transliteration and finer-grained segmentation into characters as opposed to subwords. In this work, we investigate lexical sharing in multilingual machine translation (MT) from Hindi, Gujarati, Nepali into English. We explore the trade-offs that exist in translation performance between data sampling and vocabulary size, and we explore whether transliteration is useful in encouraging cross-script generalisation. We also verify how the different settings generalise to unseen languages (Marathi and Bengali). We find that transliteration does not give pronounced improvements and our analysis suggests that our multilingual MT models trained on original scripts seem to already be robust to cross-script differences even for relatively low-resource languages
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