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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2403.14208 |
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| _version_ | 1866910377505193984 |
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| author | Nikolaus, Mitja Agrawal, Abhishek Kaklamanis, Petros Warstadt, Alex Fourtassi, Abdellah |
| author_facet | Nikolaus, Mitja Agrawal, Abhishek Kaklamanis, Petros Warstadt, Alex Fourtassi, Abdellah |
| contents | The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate more than 4,000 utterances from a large corpus of transcribed conversations. Based on these annotations, we train and evaluate a range of NLP models. Our results show that fine-tuned Transformer-based models perform best, achieving human inter-annotation agreement levels.As a first application and sanity check of this tool, we use the trained models to annotate a corpus almost two orders of magnitude larger than the manually annotated data and verify that children's grammaticality shows a steady increase with age.This work contributes to the growing literature on applying state-of-the-art NLP methods to help study child language acquisition at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14208 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic Annotation of Grammaticality in Child-Caregiver Conversations Nikolaus, Mitja Agrawal, Abhishek Kaklamanis, Petros Warstadt, Alex Fourtassi, Abdellah Computation and Language The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate more than 4,000 utterances from a large corpus of transcribed conversations. Based on these annotations, we train and evaluate a range of NLP models. Our results show that fine-tuned Transformer-based models perform best, achieving human inter-annotation agreement levels.As a first application and sanity check of this tool, we use the trained models to annotate a corpus almost two orders of magnitude larger than the manually annotated data and verify that children's grammaticality shows a steady increase with age.This work contributes to the growing literature on applying state-of-the-art NLP methods to help study child language acquisition at scale. |
| title | Automatic Annotation of Grammaticality in Child-Caregiver Conversations |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.14208 |