TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks
This addresses a long-standing issue in accounting for companies needing standardized financial reporting, but it is incremental as it builds on existing BERT-based methods with domain-specific adaptations.
The paper tackles the problem of mapping company-specific ledger accounts to a standardized chart of accounts in accounting, proposing TopoLedgerBERT, which integrates hierarchical information into sentence embeddings and uses data augmentation, resulting in superior accuracy and mean reciprocal rank compared to benchmarks.
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.