Abstract
We investigate part of speech tagging for four Arabic dialects (Gulf, Levantine, Egyptian, and Maghrebi), in an out-of-domain setting. More specifically, we look at the effectiveness of 1) upsampling the target dialect in the training data of a joint model, 2) increasing the consistency of the annotations, and 3) using word embeddings pre-trained on a large corpus of dialectal Arabic. We increase the accuracy on average by about 20 percentage points.- Anthology ID:
- 2022.wanlp-1.22
- Volume:
- Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Houda Bouamor, Hend Al-Khalifa, Kareem Darwish, Owen Rambow, Fethi Bougares, Ahmed Abdelali, Nadi Tomeh, Salam Khalifa, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 238–248
- Language:
- URL:
- https://aclanthology.org/2022.wanlp-1.22
- DOI:
- 10.18653/v1/2022.wanlp-1.22
- Cite (ACL):
- Noor Abo Mokh, Daniel Dakota, and Sandra Kübler. 2022. Improving POS Tagging for Arabic Dialects on Out-of-Domain Texts. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 238–248, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Improving POS Tagging for Arabic Dialects on Out-of-Domain Texts (Abo Mokh et al., WANLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.wanlp-1.22.pdf