Content translation looks for different metadata to transfer the contents from a template in the source document to the equivalent template in the translation.
A machine learning approach was applied in this context (T221211) to identify the mappings/alignments of parameters for the most used templates and language pairs (generated alignments).
This ticket proposes to integrate the generated alignments in Content translation as an additional criteria to consider during the parameter mapping process. When a template is added to the translation for a language pair with alignment data available,the alignments will be used to identify additional mappings that could not be identified with the default approaches. That is, metadata from templateData and parsoid will still be used anyways, the alignments will surface additional possible mappings that were not considered before.
Since the alignment information comes with probability data, we need to define a reasonable threshold. In this case I think it makes more sense to err on the side of the information being copied to the wrong parameter (high coverage) rather than being lost (high accuracy), but we may need to experiment and iterate on the exact value.
Regarding metrics, it would be great to measure how many templates can be adapted with this method, in general, and compared to those incomplete or not adapted. Depending on the complexity of this, a separate ticket can be created.