在目标语言中控制形式特征的机器翻译
Machine Translation to Control Formality Features in the Target Language
AI总结：
本研究针对英语到印地语等具有形式性差异语言的翻译问题，通过在形式性控制设置下训练双语 transformer 模型并采用自动标注扩充数据，提出了一种能有效控制目标语言形式特征的通用翻译策略。
摘要：
形式性在语言交流中起着重要作用，特别是在印地语、日语和韩语等低资源语言中。这些语言根据社会语境和关系使用正式和非正式表达来传达信息。当使用语言翻译技术从不具备形式性的源语言（如英语）翻译到具备形式性的目标语言时，形式性信息的缺失可能成为产生准确结果的挑战。本研究探讨了在使用机器学习方法从英语翻译到具有形式性的语言时，应如何解决这一问题，以印地语作为示例数据。具体做法是在形式性控制的设置下训练一个双语模型，并将其性能与在类似设置下的预训练多语言模型进行比较。由于带有真实标签的训练数据不多，采用了自动标注技术来增加数据规模。主要的建模方法涉及利用 transformer 模型，该模型已在各种自然语言处理任务中证明了其有效性。我们通过将预测的掩码标记与真实标签进行比较来评估官方形式性准确率（ACC）。该指标提供了翻译与期望输出对齐程度的定量衡量。我们的研究展示了一种通用的翻译策略，该策略考虑了目标语言中形式性的细微差别，满足了多样化的语言交流需求和场景。
英文摘要：
Formality plays a significant role in language communication, especially in low-resource languages such as Hindi, Japanese and Korean. These languages utilise formal and informal expressions to convey messages based on social contexts and relationships. When a language translation technique is used to translate from a source language that does not pertain the formality (e.g. English) to a target language that does, there is a missing information on formality that could be a challenge in producing an accurate outcome. This research explores how this issue should be resolved when machine learning methods are used to translate from English to languages with formality, using Hindi as the example data. This was done by training a bilingual model in a formality-controlled setting and comparing its performance with a pre-trained multilingual model in a similar setting. Since there are not a lot of training data with ground truth, automated annotation techniques were employed to increase the data size. The primary modeling approach involved leveraging transformer models, which have demonstrated effectiveness in various natural language processing tasks. We evaluate the official formality accuracy(ACC) by comparing the predicted masked tokens with the ground truth. This metric provides a quantitative measure of how well the translations align with the desired outputs. Our study showcases a versatile translation strategy that considers the nuances of formality in the target language, catering to diverse language communication needs and scenarios.
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。
