Description

I am wondering when Assessing the Factual Accuracy of Generated Text in https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wikifact will be publically available since it's already been 6 months. @bengoodrich

From the code (input_pipeline.py) I can see that the ParallelTextInputPipeline automatically generates the SEQUENCE_START and SEQUENCE_END tokens (which means that the input text does not need to have those special tokens).

Does ParallelTextInputPipeline also perform **_padding

1. Add CI test for building documentations (Do not ignore warnings and add spellcheck).
2. Fix docstrings with incorrect/inconsistent Sphinx format. Currently, such issues are treated as warnings in the docs building.

Hi,
Is it possible to add benchmarks of some models into documentation for comparison purposes ?
Also run time would be helpful. For example 1M iteration takes a weekend with GTX 1080.

Based on this line of code:
https://github.com/ufal/neuralmonkey/blob/master/neuralmonkey/decoders/output_projection.py#L125

Current implementation isn't flexible enough; if we train a "submodel" (e.g. decoder without attention - not containing any ctx_tensors) we cannot use the trained variables to initialize model with attention defined because the size of the dense layer matrix input become