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Contradiction Detection in Financial Reports
Fraunhofer-Gesellschaft
2023-01-23
,
Deußer, Tobias
,
Pielka, Maren
,
Pucknat, Lisa
,
Jacob, Basil
,
Dilmaghani, Tim
,
Nourimand, Mahdis
,
Kliem, Bernd
,
Loitz, Rüdiger
,
Bauckhage, Christian
,
Sifa, Rafet
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 approach that incorporates informed pre-training into its transformer-based architecture to infuse this model with additional Part-Of-Speech knowledge. Furthermore, we fine-tune the model on the public Stanford Natural Language Inference Corpus and our proprietary financial contradiction dataset. It achieves an exceptional contradiction detection F1 score of 89.55% on our real-world financial contradiction dataset, beating our several baselines by a considerable margin. During the model selection process we also test various financial-document-specific transformer models and find that they underperform the more general embedding approaches.