TY - CHAP A1 - Baumann, Timo A1 - Eller, Korbinian A1 - Gagarina, Natalia ED - Lal, Yash Kumar ED - Clark, Elizabeth ED - Iyyer, Mohit ED - Chaturvedi, Snigdha ED - Brei, Anneliese ED - Brahman, Faeze ED - Chandu, Khyathi Raghavi T1 - BERT-based Annotation of Oral Texts Elicited via Multilingual Assessment Instrument for Narratives T2 - Proceedings of the the 6th Workshop on Narrative Understanding (WNU), November 12th to November 16th 2024, Miami, Florida, USA N2 - We investigate how NLP can help annotate the structure and complexity of oral narrative texts elicited via the Multilingual Assessment Instrument for Narratives (MAIN). MAIN is a theory-based tool designed to evaluate the narrative abilities of children who are learning one or more languages from birth or early in their development. It provides a standardized way to measure how well children can comprehend and produce stories across different languages and referential norms for children between 3 and 12 years old. MAIN has been adapted to over ninety languages and is used in over 65 countries. The MAIN analysis focuses on story structure and story complexity which are typically evaluated manually based on scoring sheets. We here investigate the automation of this process using BERT-based classification which already yields promising results. Y1 - 2024 U6 - https://doi.org/10.18653/v1/2024.wnu-1.16 SP - 99 EP - 104 PB - Association for Computational Linguistics CY - Stroudsburg, PA, USA ER -