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Context-aware translation can be achieved by processing a concatenation ofconsecutive sentences with the standard Transformer architecture. This paperinvestigates the intuitive idea of providing the model with explicitinformation about the position of the sentences contained in the concatenationwindow. We compare various methods to encode sentence positions into tokenrepresentations, including novel methods. Our results show that the Transformerbenefits from certain sentence position encoding methods on English to Russiantranslation if trained with a context-discounted loss (Lupo et al., 2022).However, the same benefits are not observed in English to German. Furtherempirical efforts are necessary to define the conditions under which theproposed approach is beneficial.

Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenation