从社交媒体转移流行病预警的语言
Language Transfer for Early Warning of Epidemics from Social Media
摘要：
可以分析社交媒体上的声明，以识别出现红旗医学症状的个人，从而及早发现流感等疾病的传播。由于疾病不尊重文化边界，并且可能在讲不同语言的人群之间传播，因此我们希望建立多语言模型。然而，为每种语言训练模型所需的数据可能难以获得、昂贵且耗时，特别是对于低资源语言。以日语作为我们的目标语言，我们探索的方法，在一种语言的数据可能会被用来建立一个不同的语言模型。我们评估了机器翻译数据的训练策略和通过使用多语言模型的zero-shot传输策略。我们发现，源语言的选择影响的性能，与汉语-日语是一个更好的语言对比英语-日语。机器翻译数据的训练显示出希望，特别是当与少量目标语言数据结合使用时。
英文摘要：
Statements on social media can be analysed to identify individuals who are experiencing red flag medical symptoms, allowing early detection of the spread of disease such as influenza. Since disease does not respect cultural borders and may spread between populations speaking different languages, we would like to build multilingual models. However, the data required to train models for every language may be difficult, expensive and time-consuming to obtain, particularly for low-resource languages. Taking Japanese as our target language, we explore methods by which data in one language might be used to build models for a different language. We evaluate strategies of training on machine translated data and of zero-shot transfer through the use of multilingual models. We find that the choice of source language impacts the performance, with Chinese-Japanese being a better language pair than English-Japanese. Training on machine translated data shows promise, especially when used in conjunction with a small amount of target language data.
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。
