@inproceedings{baumann-etal-2024-bert,
title = "{BERT}-based Annotation of Oral Texts Elicited via Multilingual Assessment Instrument for Narratives",
author = "Baumann, Timo and
Eller, Korbinian and
Gagarina, Natalia",
editor = "Lal, Yash Kumar and
Clark, Elizabeth and
Iyyer, Mohit and
Chaturvedi, Snigdha and
Brei, Anneliese and
Brahman, Faeze and
Chandu, Khyathi Raghavi",
booktitle = "Proceedings of the The 6th Workshop on Narrative Understanding",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wnu-1.16",
pages = "99--104",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T BERT-based Annotation of Oral Texts Elicited via Multilingual Assessment Instrument for Narratives
%A Baumann, Timo
%A Eller, Korbinian
%A Gagarina, Natalia
%Y Lal, Yash Kumar
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Chaturvedi, Snigdha
%Y Brei, Anneliese
%Y Brahman, Faeze
%Y Chandu, Khyathi Raghavi
%S Proceedings of the The 6th Workshop on Narrative Understanding
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F baumann-etal-2024-bert
%X 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.
%U https://aclanthology.org/2024.wnu-1.16
%P 99-104
Markdown (Informal)
[BERT-based Annotation of Oral Texts Elicited via Multilingual Assessment Instrument for Narratives](https://aclanthology.org/2024.wnu-1.16) (Baumann et al., WNU 2024)
ACL