Comments (6)
I see, but I think it should be clear that you used the option for zh-en to allow others to compare the scores in a fair condition. (apologies if it's been already mentioned somewhere). Thanks a lot for your quick responses!
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Hi, could you set max_steps to 40000 and see if you can reproduce the results? If still not, could you send me your datasets ([email protected]) and I'll try to figure out the reason.
Also, just to confirm, could you reproduce the results of multilingual BERT without fine-tuning?
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Also, just to confirm, could you reproduce the results of multilingual BERT without fine-tuning?
Yes, I did in Ja-En.
However, actually I got a slightly different result in Zh-En; I got 17.9 but the paper says it is 18.1. I think this has something to do with how to handle possible alignments, which do not exist in Ja-En. In fact, using the ignorePossible option at tools/aer.py produced 18.1, and 17.9 without it. So I'm assuming you used the ignorePossible option to calculate the AER, is that correct?
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Thanks! Yes, for Zh-En that is correct.
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So did you use the --ignorePossible option for all the language pairs? If so, I think that should be noted at least in README, as it is not the standard way of calculating AER.
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Nope, for all the other language pairs I didn't use that.
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Related Issues (20)
- Parallel corpus data format for fine-tuning on parallel data HOT 1
- Continue with checkpoint HOT 2
- How to test fine tuned model on parallel data? HOT 3
- How can I resume training from a checkpoint? HOT 1
- How to get test results? HOT 2
- Extracting dataset and AttributeError HOT 3
- Abort experiment in Table 8 HOT 3
- Statistics between models HOT 1
- Trying to train on an existing model HOT 1
- Trying to train "ixa-ehu/ixambert-base-cased" model HOT 1
- Setting seq-to-seq model as our pretrained model HOT 6
- Alignment prob matrix visualization tool? HOT 2
- Question on Figure 2 in your paper HOT 2
- Is it possible align phrase by phrase, not just word by word? HOT 4
- Is it possible to incorporate POS tag info to aid alignment? HOT 1
- Inputs shoult be tokenized only for training/evaluation sets? HOT 1
- A bug(maybe) HOT 1
- Torch.save() for large training Dataset HOT 1
- Training details HOT 2
- Repeated single-sentence inferences on an in-memory model? HOT 1
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