If you don’t find an answer here, check out the issue tracker on Github.
Decoding continues after the end-of-sentence symbol¶
Double-check that the reserved IDs in your predictor are consistent with SGNMT. You can change the
reserved IDs used by SGNMT with the
indexing_scheme parameter, or mask predictors with their
own reserved IDs with the
idxmap predictor wrapper.
ImportError: No module named pywrapfst¶
SGNMT could not find the path to OpenFST, or you use a wrong OpenFST version (>=1.5.4). Make sure that
OpenFST is in your
LD_LIBRARY_PATH as explained on the Installation page.
‘No complete hypothesis found’ when combining models with different tokenizations¶
Make sure that all your word maps use the standard names for reserved tokens (
double-check that your
indexing_scheme parameter is consistent with the word maps and the indexing schemes
in your models.
‘int’ object is not callable (fst, nfst, rtn predictors)¶
You are likely to use an outdated OpenFST version. The required version is 1.5.4.
‘Dimension mismatch’ warnings with neural models¶
This warning often indicates that the NMT model configuration does not match the training configuration of the
loaded NMT model, e.g. because the model has been trained with a different vocabulary size. Double-check the NMT
configuration parameters, especially
No such file or directory: ‘test_en’¶
Per default, SGNMT tries to read the source sentences to translate from a file called ‘test_en’. You should
specify the path to the source sentences with
--src_test, or use the input methods ‘stdin’ or ‘shell’
for interactive usage.
Theano error: UnusedInputError¶
This is Blocks related and has been discussed here. The solution is
on_unused_input='ignore' to your
$ export THEANO_FLAGS="on_unused_input='ignore'"
KeyError when using NPLM¶
If you are using nplm 0.3 there might be a bug in the Python module that prevents the nplm predictor to read model files. Try to replace nplm.py in the python/ directory of your NPLM installation with this file.
Segmentation fault using SRILM¶
The swig-srilm package used in SGNMT often does not produce very helpful error messages. Usually, segmentation faults with the srilm predictor are due to a LM file which is not in the expected format. Double-check that you are using well-formed ARPA files in plain text format (not gzipped!) with word IDs.
Beam decoder with beam=12 does not create 12-best lists¶
The beam decoder stops when the best scoring hypothesis ends with the end-of-sentence symbol. Therefore, n-best lists
do not contain hypotheses longer than the best hypothesis. If you want to create full n-best lists, use
--early_stopping false. Thereby, the decoding does not stop until all active hypotheses end with the end-of-sentence symbol.