Context-aware translation can be achieved by processing a concatenation of consecutive sentences with the standard Transformer architecture. This paper investigates the intuitive idea of providing the model with explicit information about the position of the sentences contained in the concatenation window. We compare various methods to encode sentence positions into token representations, including novel methods. Our results show that the Transformer benefits from certain sentence position encoding methods on English to Russian translation if trained with a context-discounted loss (Lupo et al., 2022). However, the same benefits are not observed in English to German. Further empirical efforts are necessary to define the conditions under which the proposed approach is beneficial.

翻译：上下文感知翻译可通过标准Transformer架构处理连续句子的拼接来实现。本文探究一个直观思路：向模型提供拼接窗口内句子位置的显式信息。我们比较了多种将句子位置编码到词元表示中的方法，包括若干创新性方法。实验结果表明，若采用上下文折扣损失（Lupo等人，2022年）进行训练，在英译俄任务中Transformer能受益于特定句子位置编码方法；然而，在英译德任务中未观察到类似优势。需进一步开展实证研究以界定所提方法的适用条件。