Abstract
The parallel Bible corpus is a uniquely broad multilingual resource, covering over 1400 languages. While this data is potentially highly useful for extending language coverage in both token-based typology research and various low-resource NLP applications, the restricted register and translational nature of the Bible texts has raised concerns as to whether they are sufficiently representative of language use outside of their specific context. In this paper, we analyze the reliability and generalisability of word order statistics extracted from the Bible corpus from two angles: stability across different translations in the same language, and comparability with Universal Dependencies corpora and typological database classifications from URIEL and Grambank. We find that variation between same-language translations is generally low and that agreement with other data sources and previous work is generally high, suggesting that the impact of issues specific to massively parallel texts is smaller than previously posited.- Anthology ID:
- 2024.lrec-main.965
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 11070–11079
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.965
- DOI:
- Cite (ACL):
- Amanda Kann. 2024. Massively Multilingual Token-Based Typology Using the Parallel Bible Corpus. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11070–11079, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Massively Multilingual Token-Based Typology Using the Parallel Bible Corpus (Kann, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.965.pdf