no code implementations • LREC 2022 • Dana Abdulrahim, Go Inoue, Latifa Shamsan, Salam Khalifa, Nizar Habash
Our objective is to create a specialized corpus of the Bahraini Arabic dialect, which includes written texts as well as transcripts of audio files, belonging to a different genre (folktales, comedy shows, plays, cooking shows, etc.).
1 code implementation • 24 May 2023 • Bashar Alhafni, Go Inoue, Christian Khairallah, Nizar Habash
We also define the task of multi-class Arabic grammatical error detection (GED) and present the first results on multi-class Arabic GED.
no code implementations • 30 Nov 2022 • Ossama Obeid, Go Inoue, Nizar Habash
We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and Levantine.
1 code implementation • Findings (ACL) 2022 • Go Inoue, Salam Khalifa, Nizar Habash
We present state-of-the-art results on morphosyntactic tagging across different varieties of Arabic using fine-tuned pre-trained transformer language models.
1 code implementation • EACL (WANLP) 2021 • Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, Houda Bouamor, Nizar Habash
In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models.
1 code implementation • LREC 2020 • Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alex Erdmann, er, Nizar Habash
We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python.
no code implementations • CONLL 2017 • Go Inoue, Hiroyuki Shindo, Yuji Matsumoto
One reason for this is that in the tagging scheme for such languages, a complete POS tag is formed by combining tags from multiple tag sets defined for each morphosyntactic category.