Contradiction Detection in Financial Reports
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.
- Veröffentlicht in:
Proceedings of the Northern Lights Deep Learning Workshop 2023 - Typ:
Inproceedings - Autoren:
- Jahr:
2023 - Source:
https://septentrio.uit.no/index.php/nldl/article/view/6799
Informationen zur Zitierung
: Contradiction Detection in Financial Reports, Proceedings of the Northern Lights Deep Learning Workshop 2023, 2023, https://septentrio.uit.no/index.php/nldl/article/view/6799, Deusser.etal.2023a,
@Inproceedings{Deusser.etal.2023a,
author={Deußer, Tobias; Pielka, Maren; Pucknat, Lisa; Jacob, Basil; Dilmaghani, Tim; Nourimand, Mahdis; Kliem, Bernd; Loitz, Rüdiger; Bauckhage, Christian; Sifa, Rafet},
title={Contradiction Detection in Financial Reports},
booktitle={Proceedings of the Northern Lights Deep Learning Workshop 2023},
url={https://septentrio.uit.no/index.php/nldl/article/view/6799},
year={2023},
abstract={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...}}
