Diversity-Aware Ensembling of Language Models Based on Topological Data
  Analysis
Polina Proskura Alexey Zaytsev
Ensembles are important tools for improving the performance of machine
learning models. In cases related to natural language processing, ensembles
boost the performance of a method due to multiple large models available in
open source. However, existing approaches mostly rely on simple averaging of
predictions by ensembles with equal weights for each model, ignoring
differences in the quality and conformity of models. We propose to estimate
weights for ensembles of NLP models using not only knowledge of their
individual performance but also their similarity to each other. By adopting
distance measures based on Topological Data Analysis (TDA), we improve our
ensemble. The quality improves for both text classification accuracy and
relevant uncertainty estimation.