Hi @guillaumekln !
We've tried the ensemble decoding.
Regarding to the warning in the documentation...
do the models have to share also the source vocabulary as well?
If not, maybe some of them are not going to understand the input sentence to translate.
I mean, for instance, label 23 can be 'house' for model1 but 'car' for model2, and if you have the source codified using the source dictionary from model1, then model2 will understand a different sentence to translate because it will be using the representations for word 'car' and not 'house', doesn't it?
Apart from that, I've tried it and it works fine.
I have used 4 models from the same training for English to Spanish translation models.
In terms of speed, it works around 4 times slower than single decoding.
In terms of BLEU, we gain 0.2 .
And in terms of TER, we gain 0.4 .
Does that resemble your results?
thank you very much!
this will be very helpful for future experiments!