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  4. ALiBERT: Improved automated list inspection (ALI) with BERT
 
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2021
Conference Paper
Titel

ALiBERT: Improved automated list inspection (ALI) with BERT

Abstract
We consider Automated List Inspection (ALI), a content-based text recommendation system that assists auditors in matching relevant text passages from notes in financial statements to specific law regulations. ALI follows a ranking paradigm in which a fixed number of requirements per textual passage are shown to the user. Despite achieving impressive ranking performance, the user experience can still be improved by showing a dynamic number of recommendations. Besides, existing models rely on a feature-based language model that needs to be pre-trained on a large corpus of domain-specific datasets. Moreover, they cannot be trained in an end-to-end fashion by jointly optimizing with language model parameters. In this work, we alleviate these concerns by considering a multi-label classification approach that predicts dynamic requirement sequences. We base our model on pre-trained BERT that allows us to fine-tune the whole model in an end-to-end fashion, thereby avoiding the need for training a language representation model. We conclude by presenting a detailed evaluation of the proposed model on two German financial datasets.
Author(s)
Ramamurthy, R.
Pielka, M.
Stenzel, R.
Bauckhage, C.
Sifa, R.
Khameneh, T.D.
Warning, U.
Kliem, B.
Loitz, R.
Hauptwerk
DocEng 2021, ACM Symposium on Document Engineering. Proceedings
Konferenz
Symposium on Document Engineering (DocEng) 2021
Thumbnail Image
DOI
10.1145/3469096.3474928
Language
English
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Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
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