scripts/EMBEDDING_SUMMARY.md

This approach ensures:

• Consistent character positioning: Character t is always at position 1 relative to other characters
• Feature invariance: Features don't interfere with character-to-character relationships
• Order independence: Feature order doesn't affect character positioning
• Better attention patterns: The model can focus on character relationships without feature interference

Implementation Success ✅

The improved TagTransformer has been successfully implemented and tested with your example:

t ɾ a d ˈ u s e n <V;IND;PRS;3;PL> # t ɾ a d u s k ˈ a m o s <V;SBJV;PRS;1;PL> # <V;SBJV;PRS;3;PL>

Key Results:

• 26 tokens processed correctly
• Features get position 0: All tags and separators have Char Pos: 0
• Characters get sequential positions: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
• Special embeddings work: Features get different special embeddings than characters
• Consistent relative distances: Character t is always at position 1 relative to other characters, regardless of feature placement

This implementation successfully addresses the problem described in the paper: "To avoid such an inconsistency, we propose a simple remedy: We set the positional encoding of features to 0 and only start counting the positions for characters."