用嵌入对齐法实现英-约鲁巴语词级翻译，揭示低资源语言翻译的关键影响因素。
Edeflip: Supervised Word Translation between English and Yoruba
- 采用监督嵌入对齐方法实现英到约鲁巴语的词翻译。
- 高质量嵌入与归一化显著提升翻译准确率，二者存在交互作用。
- 为低资源语言机器翻译研究提供重要参考，强调优质单语嵌入的重要性。
近年来，嵌入对齐已成为无需平行语料训练的先进机器翻译方法，可生成高质量翻译。然而，现有研究和应用主要集中在具备高质量单语嵌入的高资源语言上，低资源语言是否同样受益尚不明确。本研究将成熟的监督嵌入对齐方法应用于英语到约鲁巴语的词翻译任务，后者是一种低资源语言。结果表明，更高的嵌入质量与嵌入归一化均能提升词翻译精度，且两者存在交互效应。研究揭示了当前最先进的监督嵌入对齐在低资源语言上的局限性，指出需额外考虑如单语嵌入质量等关键因素。我们希望本工作能成为未来关注低资源语言挑战的机器翻译研究的起点。
原文摘要 · Abstract (English)
In recent years, embedding alignment has become the state-of-the-art machine translation approach, as it can yield high-quality translation without training on parallel corpora. However, existing research and application of embedding alignment mostly focus on high-resource languages with high-quality monolingual embeddings. It is unclear if and how low-resource languages may be similarly benefited. In this study, we implement an established supervised embedding alignment method for word translation from English to Yoruba, the latter a low-resource language. We found that higher embedding quality and normalizing embeddings increase word translation precision, with, additionally, an interaction effect between the two. Our results demonstrate the limitations of the state-of-the-art supervised embedding alignment when it comes to low-resource languages, for which there are additional factors that need to be taken into consideration, such as the importance of curating high-quality monolingual embeddings. We hope our work will be a starting point for further machine translation research that takes into account the challenges that low-resource languages face.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。