  Representing text into a multidimensional space can be done with sentence
embedding models such as Sentence-BERT (SBERT). However, training these models
when the data has a complex multilevel structure requires individually trained
class-specific models, which increases time and computing costs. We propose a
two step approach which enables us to map sentences according to their
hierarchical memberships and polarity. At first we teach the upper level
sentence space through an AdaCos loss function and then finetune with a novel
loss function mainly based on the cosine similarity of intra-level pairs. We
apply this method to three different datasets: two weakly supervised Big Five
personality dataset obtained from English and Japanese Twitter data and the
benchmark MNLI dataset. We show that our single model approach performs better
than multiple class-specific classification models.
