Comments (4)
BPE always starts with a character-level segmentation so you start with {d i r e c t} and apply pairwise merge operations until you've reached the maximum number of merge operations (at training time), or until there is no more valid merge operation in your learned list (at test time).
So I'm not sure why you're asking about the smallest subunits (this is always characters). Instead, maybe you should be interested in the largest subword units that are still in-vocabulary. If your segmentation produces "di@@ rect", then "di@@" and "rect" are both in-vocabulary subword units, but "direct" is out-of-vocabulary.
from subword-nmt.
Thank you for your response, you are right, it is more relevant to think about the largest subwords.
I understand your explanation. I hope I don't bother you too much with another question. Just so I get my understanding right, if the final segmentation would be di@@ rec@@ t
, how would I know if rec
would be the largest subunit, or could it also be re
(belonging to the first binary pair di
-re
) and c
(belonging to the second binary pair c
-t
), but rec
is not a learned subword?
from subword-nmt.
I'm not sure I get your question.
The final segmentation is produced by a greedy algorithm that iteratively applies the most frequent pairwise merge operation that has been learned on the training set. If you reach the intermediate segmentation (di - re - c - t), and di - re and c - t are the most frequent subword pairs and merged by the algorithm next (instead of re - c), the final segmentation would be dire@@ ct.
from subword-nmt.
Thank you, your explanations helped me understanding how you program works!
from subword-nmt.
Related Issues (20)
- subword-nmt HOT 3
- learn_bpe.py error HOT 1
- learn_joint_bpe_and_vocab.py for Japanese HOT 1
- BPE-Dropout question HOT 1
- Recover back code file HOT 1
- DeprecationWarning and ResourceWarning: Enable tracemalloc to get the object allocation traceback HOT 2
- No module named apply_bpe HOT 4
- About the vocabulary size HOT 2
- Readme update please HOT 1
- Question about vocabulary filter HOT 2
- Unknown word and vocabulary filter HOT 2
- How to avoid special char like '\t' being split by bpe HOT 3
- applying BPE(Byte Pair Encoding) fails for large Chinese data tokenized with THULAC HOT 1
- Error: invalid line 2 in BPE codes file when running apply_bpe.py HOT 1
- How to decode BPE when applied to machine translation HOT 1
- learn_bpe.py code question HOT 1
- BrokenPipeError: [Errno 32] Broken pipe HOT 3
- Is it possibile to extend a trained BPE model's merge operations? HOT 2
- How to find all valid BPEs for a word? HOT 3
- AttributeError: 'BPE' object has no attribute 'glossaries_regex' HOT 1
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