Before revision:  We contribute to the field of contrastive learning by extending it to multiple correspondence analysis (MCA)  to enable an analysis of data often encountered by social scientists---namely binary, ordinal, and nominal variables.
After revision:  We contribute to this emerging field by extending its ideas to multiple correspondence analysis (MCA) to enable an analysis of data often encountered by social scientists---namely binary, ordinal, and nominal variables.