# Potentially interesting further work

We finally discuss the potential implications of the current version of autoBOT, how and where it could be further used.

## Extensions with contextual neural representations

Current version of autoBOT does not include any Transformer-based e.g., sentence representations. Adding this functionality is one API call away, and could notably boost the performance.

## Improving the classification phase

Given that the current implementation of autoBOT includes the SGD-based linear learners, a natural extension at this point is the use of more involved classifiers. Current implementation (see examples) offers this functionality out-of-the-box.

## Speeding up evolution

Current implementation of evolution is one of the most basic ones. Should more involved, potentially multi-objective scenarios be considered, it’s possible this step can be drastically improved via e.g., inclusion of Pareto front-based traversals etc.

## Meta-transfer learning

Given that current implementation of autoBOT results in solution vectors that potentially represent the representation space suitable for a given document, we believe that using such information as priors could speed up the evolution on novel data sets from the same domain.