These are features we are keen on! Check out issues in github to see status of things.
- easier deployment options
- create a platform for rasa NLU users to share models/ data
- support loading training data from a DB instead of a text file
- entity normalisation: as is, the named entity extractor will happily extract cheap & inexpensive as entities of the expense class, but will not tell you that these are realisations of the same underlying concept. You can easily handle that with a list of aliases in your code, but we want to offer a more elegant & generalisable solution. [Word Forms](https://github.com/gutfeeling/word_forms) looks promising.
- parsing structured data, e.g. dates. We might use [parsedatetime](https://pypi.python.org/pypi/parsedatetime/) or [parserator](https://github.com/datamade/parserator) or wit.ai’s very own [duckling](https://duckling.wit.ai/).
- python 3 support
- support for more (human) languages