TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks

TL;DR

TopoLedgerBERT is a novel hierarchical sentence embedding method that leverages Siamese BERT networks and data augmentation to improve ledger account mapping accuracy.

Contribution

It introduces a new embedding approach that incorporates hierarchical chart information and data augmentation for better ledger account alignment.

Findings

Outperforms benchmark methods in accuracy

Achieves higher mean reciprocal rank

Effectively captures hierarchical and semantic information

Abstract

This paper addresses a long-standing problem in the field of accounting: mapping company-specific ledger accounts to a standardized chart of accounts. We propose a novel solution, TopoLedgerBERT, a unique sentence embedding method devised specifically for ledger account mapping. This model integrates hierarchical information from the charts of accounts into the sentence embedding process, aiming to accurately capture both the semantic similarity and the hierarchical structure of the ledger accounts. In addition, we introduce a data augmentation strategy that enriches the training data and, as a result, increases the performance of our proposed model. Compared to benchmark methods, TopoLedgerBERT demonstrates superior performance in terms of accuracy and mean reciprocal rank.