Hello, excuse me for the interruption. Could you please explain the function of PROMPT_MAPPING mentioned in constants.py?

[ChenLBs]

GRAPHIX_RELATIONS = ['question-question-dist-1', 'question-column-star', 'question-question-modifier', 'question-question-argument', 
'question-table-exactmatch', 'question-column-partialmatch', 'question-column-valuematch', 'table-question-exactmatch', 'table-column-pk', 
'table-column-has', 'table-table-fkr', 'column-question-partialmatch', 'column-table-pk', 'column-column-sametable', 'column-column-fkr', 
'column-column-star', 'column-table-has', 'column-question-valuematch', 'table-table-fk', 'column-column-fk', 'column-question-star', 
'question-column-exactmatch', 'column-question-exactmatch', 'question-table-partialmatch', 'table-question-partialmatch']

PROMPT_MAPPING = {
    'question-question-dist-1': 'This question word is the closet neighbor of another question word.',
    'question-column-star' : "This question word is connected with the special column item: '*'.",
    'question-question-modifier': 'The modifier of this question word is another word.',
    'question-question-argument': 'The argument of this question word is another word.',
    'question-table-exactmatch': 'This question word is matched exactly with the table item.',
    'question-column-partialmatch': 'This question word is matched partially with the column item.',
    'question-column-valuematch': 'This question word is matched with one of values in this column item.',
    'table-question-exactmatch': 'This table item is matched exactly with the question word.',
    'table-column-pk': 'This table item contains this column item as a primary key.',
    'table-column-has': 'This table item owns this column item as a normal relation.',
    'table-table-fkr': 'The other table item is linked with this table item by a foreign key.',
    'column-question-partialmatch': 'This column item is matched partially with the question word.',
    'column-table-pk': 'This column item is the unique primary key of the table item.',
    'column-column-sametable': 'This column item and the other column item appear in the same table.',
    'column-column-fkr': 'The other column item and this column item is linked as foreign key.',
    'column-column-star': "This column item is special column item: '*' or the other column item is the special column item: '*'.",
    'column-table-has': 'This column item belongs to the table item.',
    'column-question-valuematch': 'This column item contains the value that are matched with the question word.',
    'table-table-fk': 'This table item is linked with the other table item by a foreign key.',
    'column-column-fk': 'This column item and the other column item is linked as foreign key.',
    'column-question-star': "This speical column item: '*' links with the question word.",
    'question-column-exactmatch': 'This question word is matched exactly with the column item.',
    'column-question-exactmatch': 'This column item is matched exactly with the question word.',
    'question-table-partialmatch': 'This question word is matched partially with the table item.',
    'table-question-partialmatch': 'This table item is matched partially with the question word.'
}

I didn't see any calls, is this a technique you use to enhance performance?

[accpatrick]

@ChenLBs Thanks for your interest in our work and for asking insightful questions! We didn't actually use this file in our implementation. It represents an experimental approach we took to initialize the relation_embedding. In the original paper, the relation_embedding was initialized randomly, which already achieved the SOTA on both EM and EX at that time. However, a more refined method would be to convert these relations into natural language and use the T5 model's representations of these sentences as the initial embeddings.