TY - JOUR U1 - Wissenschaftlicher Artikel A1 - Ali, Syed Musharraf A1 - Deußer, Tobias A1 - Houben, Sebastian A1 - Hillebrand, Lars A1 - Metzler, Tim A1 - Sifa, Rafet T1 - Automatic Consistency Checking of Table and Text in Financial Documents JF - Proceedings of the Northern Lights Deep Learning Workshop N2 - A company's financial documents use tables along with text to organize the data containing key performance indicators (KPIs) (such as profit and loss) and a financial quantity linked to them. The KPI’s linked quantity in a table might not be equal to the similarly described KPI's quantity in a text. Auditors take substantial time to manually audit these financial mistakes and this process is called consistency checking. As compared to existing work, this paper attempts to automate this task with the help of transformer-based models. Furthermore, for consistency checking it is essential for the table's KPIs embeddings to encode the semantic knowledge of the KPIs and the structural knowledge of the table. Therefore, this paper proposes a pipeline that uses a tabular model to get the table's KPIs embeddings. The pipeline takes input table and text KPIs, generates their embeddings, and then checks whether these KPIs are identical. The pipeline is evaluated on the financial documents in the German language and a comparative analysis of the cell embeddings' quality from the three tabular models is also presented. From the evaluation results, the experiment that used the English-translated text and table KPIs and Tabbie model to generate table KPIs’ embeddings achieved an accuracy of 72.81% on the consistency checking task, outperforming the benchmark, and other tabular models. KW - deep learning KW - natural language processing KW - text mining Y1 - 2023 UN - https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-65950 SN - 2703-6928 SS - 2703-6928 U6 - https://doi.org/10.7557/18.6816 DO - https://doi.org/10.7557/18.6816 VL - 4 SP - 9 S1 - 9 PB - Septentrio Academic Publishing CY - Tromsø, Norway ER -