Warning: This document is for an old version of Rasa NLU. The latest version is 0.15.1.

Language Support

Rasa NLU can be used to understand any language, but some backends are restricted to specific languages.

The tensorflow_embedding pipeline can be used for any language, because it trains custom word embeddings for your domain.

Pre-trained Word Vectors

With the spaCy backend you can now load fastText vectors, which are available for hundreds of languages.

backend supported languages
spacy-sklearn english (en), german (de), spanish (es), portuguese (pt), italian (it), dutch (nl), french (fr)
MITIE english (en)
Jieba-MITIE chinese (zh) *

These languages can be set as part of the Server Configuration.

Adding a new language

We want to make the process of adding new languages as simple as possible to increase the number of supported languages. Nevertheless, to use a language you either need a trained word representation or you need to train that presentation on your own using a large corpus of text data in that language.

These are the steps necessary to add a new language:


spaCy already provides a really good documentation page about Adding languages. This will help you train a tokenizer and vocabulary for a new language in spaCy.

As described in the documentation, you need to register your language using set_lang_class() which will allow Rasa NLU to load and use your new language by passing in your language identifier as the language Server Configuration option.


  1. Get a ~clean language corpus (a Wikipedia dump works) as a set of text files
  2. Build and run MITIE Wordrep Tool on your corpus. This can take several hours/days depending on your dataset and your workstation. You’ll need something like 128GB of RAM for wordrep to run - yes that’s alot: try to extend your swap.
  3. Set the path of your new total_word_feature_extractor.dat as value of the mitie_file parameter in config_mitie.json


Some notes about using the Jieba tokenizer together with MITIE on chinese language data: To use it, you need a proper MITIE feature extractor, e.g. data/total_word_feature_extractor_zh.dat. It should be trained from a Chinese corpus using the MITIE wordrep tools (takes 2-3 days for training).

For training, please build the MITIE Wordrep Tool. Note that Chinese corpus should be tokenized first before feeding into the tool for training. Close-domain corpus that best matches user case works best.

A detailed instruction on how to train the model yourself can be found in A trained model from Chinese Wikipedia Dump and Baidu Baike can be crownpku ‘s blogpost.

Have questions or feedback?

We have a very active support community on Rasa Community Forum that is happy to help you with your questions. If you have any feedback for us or a specific suggestion for improving the docs, feel free to share it by creating an issue on Rasa NLU GitHub repository.