However, #5 was worse than #2, despite being trained in the same way, because #5 involves up to eight target languages, inevitably reducing the allowance for each target language.
exploiting the multiparallel corpus through mixed pre-training (#6) or mixed fine-tuning (#7) brought consistent improvements over the corresponding 1-to-2 models (#3 and #4).