Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916522771873792 |
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| author | Mahmudi, Aso Herce, Borja Amestica, Demian Inostroza Scherbakov, Andreas Hovy, Eduard Vylomova, Ekaterina |
| author_facet | Mahmudi, Aso Herce, Borja Amestica, Demian Inostroza Scherbakov, Andreas Hovy, Eduard Vylomova, Ekaterina |
| contents | Linguistic fieldwork is an important component in language documentation and preservation. However, it is a long, exhaustive, and time-consuming process. This paper presents a novel model that guides a linguist during the fieldwork and accounts for the dynamics of linguist-speaker interactions. We introduce a novel framework that evaluates the efficiency of various sampling strategies for obtaining morphological data and assesses the effectiveness of state-of-the-art neural models in generalising morphological structures. Our experiments highlight two key strategies for improving the efficiency: (1) increasing the diversity of annotated data by uniform sampling among the cells of the paradigm tables, and (2) using model confidence as a guide to enhance positive interaction by providing reliable predictions during annotation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14628 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection Mahmudi, Aso Herce, Borja Amestica, Demian Inostroza Scherbakov, Andreas Hovy, Eduard Vylomova, Ekaterina Computation and Language Linguistic fieldwork is an important component in language documentation and preservation. However, it is a long, exhaustive, and time-consuming process. This paper presents a novel model that guides a linguist during the fieldwork and accounts for the dynamics of linguist-speaker interactions. We introduce a novel framework that evaluates the efficiency of various sampling strategies for obtaining morphological data and assesses the effectiveness of state-of-the-art neural models in generalising morphological structures. Our experiments highlight two key strategies for improving the efficiency: (1) increasing the diversity of annotated data by uniform sampling among the cells of the paradigm tables, and (2) using model confidence as a guide to enhance positive interaction by providing reliable predictions during annotation. |
| title | Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.14628 |