# Towards conceptual generalization in the embedding space

## TL;DR

This paper explores unsupervised deep learning methods to create a unified conceptual embedding space that captures relations between different levels of notions, enabling a geometric understanding of cognition without parallel data.

## Contribution

It introduces a novel approach to align multiple embedding spaces in an unsupervised manner to model multi-level conceptual relations.

## Key findings

- Successfully built one-to-many maps between notion sets
- Established a unified vector representation of the outer world
- Demonstrated geometric relations between cognitive objects

## Abstract

Humans are able to conceive physical reality by jointly learning different facets thereof. To every pair of notions related to a perceived reality may correspond a mutual relation, which is a notion on its own, but one-level higher. Thus, we may have a description of perceived reality on at least two levels and the translation map between them is in general, due to their different content corpus, one-to-many. Following success of the unsupervised neural machine translation models, which are essentially one-to-one mappings trained separately on monolingual corpora, we examine further capabilities of the unsupervised deep learning methods used there and apply some of these methods to sets of notions of different level and measure. Using the graph and word embedding-like techniques, we build one-to-many map without parallel data in order to establish a unified vector representation of the outer world by combining notions of different kind into a unique conceptual framework. Due to their latent similarity, by aligning the two embedding spaces in purely unsupervised way, one obtains a geometric relation between objects of cognition on the two levels, making it possible to express a natural knowledge using one description in the context of the other.