The issue I met when do the NER task for Universal language by using XLM-R
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**A link to original question on Stack Overflow**: 
Firstly, go through the roughly logic when we do slot tagging task for English query, take query “what’s the weather in Beijing” and detect location slot tagging as example:
1. We are using space to split the query and label it as “O O O O B-location” when generate the training data
2. By using sentence piece to tokenize the query to ['_what', "'", 's', '_the', '_weather', '_in', '_bei', 'jing'], and corresponding tagging mask [1, 0, 0, 1, 1, 1, 1, 0] (take first token in the word as 1, other tokens in the word as 0)
3. By using NER task to do the predict, and then extract the token’s prediction result as the word prediction result if the token’s mask is 1
 
When we do the NER task as above logic for CJK language, take “今天北京天气怎么样？” as example, after sentence piece tokenize, the tokens is ['▁', '北京', '今天', '天气', '怎么样', '?'], since there are no space in the query, so it will take the whole query as one slot and the corresponding tagging mask is [1, 0, 0, 0, 0, 0], so we can’t get the location slot “北京”
 
The cause is that the split methods for English and CJK are different, In French, “Je t’aime” is means “I love you”, “Je” means “I”, “t’” means “you”, “aime” means “love”, it is using “’” as splitter.

I think we can solve it by using below solutions:
1. Detect what language it is firstly for the query
2. For the language likes English, we don’t change the logic as above, for the language likes CJK, after split by using space, we can use tokens generated by sentence piece as word,
a. For example “北京今天天气怎么样，西雅图呢?”, split it as [“北京今天天气怎么样”, “西雅图呢?”] as space
b. For “北京今天天气怎么样”,  after tokenized is: ['▁', '北京', '今天', '天气', '怎么样', ',’], we mark each token as word we need to predict, the correspond tagging mask is [0, 1, 1, 1, 1, 0]
By using NER task to do the predict, and then extract the token’s prediction result as the word prediction result if the token’s mask is 1, so we can predict ‘北京’ as location slot

But for this, it needs to have specific language knowledge and language classification detector, any better ideas do you have?
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