Transformer architectures are highly expressive because they use self-attention
mechanisms to encode long-range dependencies in the input sequences. In this paper, we …

Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

This paper proposes the concept of artificial intelligence co-piloted auditing, emphasizing
the collaborative potential of auditors and foundation models in the auditing domain. The …

We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation
Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis …

Relation extraction (RE) involves identifying the relations between entities from unstructured
texts. RE serves as the foundation for many natural language processing (NLP) applications …

Auditing financial documents is a very tedious and time-consuming process. As of today, it
can already be simplified by employing AI-based solutions to recommend relevant text …

Finding and amending contradictions in a financial report is crucial for the publishing
company and its financial auditors. To automate this process, we introduce a novel …

Ever-larger language models with ever-increasing capabilities are by now well-established
text processing tools. Alas, information extraction tasks such as named entity recognition are …

Purpose This article introduces SigBERT, a novel approach that fine-tunes bidirectional
encoder representations from transformers (BERT) for the purpose of distinguishing …

Enterprise risk management holds significant importance in fostering sustainable growth of
businesses and in serving as a critical element for regulatory bodies to uphold market order …