I was checking BERT github page and noticed that there are new models built from a new training technique called "whole word masking". Here is a snippet describing it:

In the original pre-processing code, we randomly select WordPiece tokens to mask. For example:

Input Text: the man jumped up , put his basket on phil ##am ##mon ' s head Original Masked Input:
 [MASK] man [MASK] up , put his [MASK] on phil [MASK] ##mon ' s head

The new technique is called Whole Word Masking. In this case, we always mask all of the the tokens corresponding to a word at once. The overall masking rate remains the same.

Whole Word Masked Input: the man [MASK] up , put his basket on [MASK] [MASK] [MASK] ' s head

I can't understand "we always mask all of the the tokens corresponding to a word at once". "jumped", "phil", "##am", and "##mon" are masked and I am not sure how these tokens are related

I was checking BERT GitHub page and noticed that there are new models built from a new training technique called "whole word masking". Here is a snippet describing it:

In the original pre-processing code, we randomly select WordPiece tokens to mask. For example:

Input Text: the man jumped up , put his basket on phil ##am ##mon ' s head 

Original Masked Input: [MASK] man [MASK] up , put his [MASK] on phil [MASK] ##mon ' s head

The new technique is called Whole Word Masking. In this case, we always mask all of the the tokens corresponding to a word at once. The overall masking rate remains the same.

Whole Word Masked Input: the man [MASK] up , put his basket on [MASK] [MASK] [MASK] ' s head

I can't understand "we always mask all of the the tokens corresponding to a word at once". "jumped", "phil", "##am", and "##mon" are masked and I am not sure how these tokens are related.

I was checking BERT github page and noticed that there are new models built from a new training technique called "whole word masking". Here is a snippet describing it:

In the original pre-processing code, we randomly select WordPiece tokens to mask. For example:

Input Text: the man jumped up , put his basket on phil ##am ##mon ' s head Original Masked Input: [MASK] man [MASK] up , put his [MASK] on phil [MASK] ##mon ' s head
 
The new technique is called Whole Word Masking. In this case, we always mask all of the the tokens corresponding to a word at once. The overall masking rate remains the same.

Whole Word Masked Input: the man [MASK] up , put his basket on [MASK] [MASK] [MASK] ' s head

I can't understand "we always mask all of the the tokens corresponding to a word at once". "jumped", "phil", "##am", and "##mon" are masked and I am not sure how these tokens are related

I was checking BERT github page and noticed that there are new models built from a new training technique called "whole word masking". Here is a snippet describing it:

In the original pre-processing code, we randomly select WordPiece tokens to mask. For example:

Input Text: the man jumped up , put his basket on phil ##am ##mon ' s head Original Masked Input: 
[MASK] man [MASK] up , put his [MASK] on phil [MASK] ##mon ' s head

The new technique is called Whole Word Masking. In this case, we always mask all of the the tokens corresponding to a word at once. The overall masking rate remains the same.

Whole Word Masked Input: the man [MASK] up , put his basket on [MASK] [MASK] [MASK] ' s head

I can't understand "we always mask all of the the tokens corresponding to a word at once". "jumped", "phil", "##am", and "##mon" are masked and I am not sure how these tokens are related