Papers by Emi Baylor
The Past, Present, and Future of Typological Databases in NLP (2023.findings-emnlp)
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| Challenge: | Typological information is inconsistent with each other and other sources of typological information, such as linguistic grammars. |
| Approach: | They propose to examine disagreements between typological databases and their uses in NLP by exploring disagreements across databases and resources. |
| Outcome: | The proposed view of typology has significant potential in the future, including in language modeling in low-resource scenarios. |
Multilingual Gradient Word-Order Typology from Universal Dependencies (2024.eacl-short)
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| Challenge: | Existing typological databases, including WALS and Grambank, suffer from inconsistencies due to categorical format. |
| Approach: | They propose a new seed dataset that uses continuous-valued data instead of categorical data to better reflect the variability of language. |
| Outcome: | The proposed dataset can be easily adapted to generate data for a broader set of features and languages. |