1 code implementation • GeBNLP (COLING) 2020 • Bashar Alhafni, Nizar Habash, Houda Bouamor
In this paper, we present an approach for sentence-level gender reinflection using linguistically enhanced sequence-to-sequence models.
no code implementations • 29 Apr 2024 • Bashar Alhafni, Reem Hazim, Juan Piñeros Liberato, Muhamed Al Khalil, Nizar Habash
We present the SAMER Corpus, the first manually annotated Arabic parallel corpus for text simplification targeting school-aged learners.
1 code implementation • 26 Feb 2024 • Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni, Bashar Alhafni, Dhruv Kumar
We introduce mEdIT, a multi-lingual extension to CoEdIT -- the recent state-of-the-art text editing models for writing assistance.
1 code implementation • 7 Feb 2024 • Bashar Alhafni, Vivek Kulkarni, Dhruv Kumar, Vipul Raheja
As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized.
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 • 7 May 2023 • Hazem Ibrahim, Fengyuan Liu, Rohail Asim, Balaraju Battu, Sidahmed Benabderrahmane, Bashar Alhafni, Wifag Adnan, Tuka Alhanai, Bedoor AlShebli, Riyadh Baghdadi, Jocelyn J. Bélanger, Elena Beretta, Kemal Celik, Moumena Chaqfeh, Mohammed F. Daqaq, Zaynab El Bernoussi, Daryl Fougnie, Borja Garcia de Soto, Alberto Gandolfi, Andras Gyorgy, Nizar Habash, J. Andrew Harris, Aaron Kaufman, Lefteris Kirousis, Korhan Kocak, Kangsan Lee, Seungah S. Lee, Samreen Malik, Michail Maniatakos, David Melcher, Azzam Mourad, Minsu Park, Mahmoud Rasras, Alicja Reuben, Dania Zantout, Nancy W. Gleason, Kinga Makovi, Talal Rahwan, Yasir Zaki
Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to their propensity to classify human-written answers as AI-generated, as well as the ease with which AI-generated text can be edited to evade detection.
1 code implementation • 25 Oct 2022 • Hossein Rajaby Faghihi, Bashar Alhafni, Ke Zhang, Shihao Ran, Joel Tetreault, Alejandro Jaimes
This paper presents CrisisLTLSum, the largest dataset of local crisis event timelines available to date.
no code implementations • 22 Oct 2022 • Bashar Alhafni, Nizar Habash, Houda Bouamor, Ossama Obeid, Sultan Alrowili, Daliyah AlZeer, Khawlah M. Alshanqiti, Ahmed ElBakry, Muhammad ElNokrashy, Mohamed Gabr, Abderrahmane Issam, Abdelrahim Qaddoumi, K. Vijay-Shanker, Mahmoud Zyate
In this paper, we present the results and findings of the Shared Task on Gender Rewriting, which was organized as part of the Seventh Arabic Natural Language Processing Workshop.
no code implementations • 19 Oct 2022 • Reem Hazim, Hind Saddiki, Bashar Alhafni, Muhamed Al Khalil, Nizar Habash
This demo paper presents a Google Docs add-on for automatic Arabic word-level readability visualization.
no code implementations • 14 Oct 2022 • Bashar Alhafni, Ossama Obeid, Nizar Habash
We introduce the User-Aware Arabic Gender Rewriter, a user-centric web-based system for Arabic gender rewriting in contexts involving two users.
no code implementations • 5 Jul 2022 • Malihe Alikhani, Thomas Kober, Bashar Alhafni, Yue Chen, Mert Inan, Elizabeth Nielsen, Shahab Raji, Mark Steedman, Matthew Stone
Typologically diverse languages offer systems of lexical and grammatical aspect that allow speakers to focus on facets of event structure in ways that comport with the specific communicative setting and discourse constraints they face.
1 code implementation • NAACL 2022 • Bashar Alhafni, Nizar Habash, Houda Bouamor
In this paper, we define the task of gender rewriting in contexts involving two users (I and/or You) - first and second grammatical persons with independent grammatical gender preferences.
no code implementations • LREC 2022 • Bashar Alhafni, Nizar Habash, Houda Bouamor
Much of the research on this issue has focused on mitigating gender bias in English NLP models and systems.
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 • 26 Apr 2019 • Rujun Han, Mengyue Liang, Bashar Alhafni, Nanyun Peng
In this work, we establish strong baselines for event temporal relation extraction on two under-explored story narrative datasets: Richer Event Description (RED) and Causal and Temporal Relation Scheme (CaTeRS).
no code implementations • 1 Jan 2019 • Bashar Alhafni, Saulo Fernando Guedes, Lays Cavalcante Ribeiro, Juhyun Park, Jeongkyu Lee
The goal of this paper is to implement a system, titled as Drone Map Creator (DMC) using Computer Vision techniques.