Promoting Target Data in Context-aware Neural Machine Translation
<|reference_start|>Promoting Target Data in Context-aware Neural Machine Translation: Standard context-aware neural machine translation (NMT) typically relies on parallel document-level data, exploiting both source and target contexts. Concatenation-based approaches in particular, still a strong baseline for document-level NMT, prepend source and/or target context sentences to the sentences to be translated, with model variants that exploit equal amounts of source and target data on each side achieving state-of-the-art results. In this work, we investigate whether target data should be further promoted within standard concatenation-based approaches, as most document-level phenomena rely on information that is present on the target language side. We evaluate novel concatenation-based variants where the target context is prepended to the source language, either in isolation or in combination with the source context. Experimental results in English-Russian and Basque-Spanish show that including target context in the source leads to large improvements on target language phenomena. On source-dependent phenomena, using only target language context in the source achieves parity with state-of-the-art concatenation approaches, or slightly underperforms, whereas combining source and target context on the source side leads to significant gains across the board.<|reference_end|>
@article{gete2024promoting,
    title={Promoting Target Data in Context-aware Neural Machine Translation},
    author={Harritxu Gete and Thierry Etchegoyhen},
    journal={arXiv preprint arXiv:2402.06342},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06342},
    primaryClass={cs.CL}
}