University of Regensburg @ SwissText 2021 SEPP-NLG: Adding Sentence Structure to Unpunctuated Text

URN to cite this document:: urn:nbn:de:bvb:355-epub-493520

Donabauer, Gregor ; Kruschwitz, Udo

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Date of publication of this fulltext: 08 Oct 2021 05:20

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Item type:	Conference or workshop item (Paper)
Title of Book:	SwissText 2021. Proceedings of the Swiss Text Analytics Conference 2021 Winterthur, Switzerland, June 14-16, 2021 (held online due to COVID19 pandemic)
Publisher:	RWTH Aachen
Place of Publication:	Aachen
Other Series:	CEUR workshop proceedings
Volume:	2957
Date:	June 2021
Institutions:	Languages and Literatures > Institut für Information und Medien, Sprache und Kultur (I:IMSK) > Lehrstuhl für Informationswissenschaft (Prof. Dr. Udo Kruschwitz) Informatics and Data Science > Department Human-Centered Computing > Lehrstuhl für Informationswissenschaft (Prof. Dr. Udo Kruschwitz)
Keywords:	language-specific BERT models, text processing, language
Dewey Decimal Classification:	000 Computer science, information & general works > 020 Library & information sciences
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Yes
Item ID:	49352

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Abstract

This paper describes our approach (UR- mSBD) to address the shared task on Sentence End and Punctuation Prediction in NLG Text (SEPP-NLG) organised as part of SwissText 2021. We participated in Subtask 1 (fully un- punctuated sentences – full stop detection) and submitted a run for every featured language (English, German, French, Italian). Our sub- missions are based on pre-trained BERT ...