In this paper, we present an approach for sentence-level gender reinflection using linguistically enhanced sequence-to-sequence models.
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.
This demo paper presents a Google Docs add-on for automatic Arabic word-level readability visualization.
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.
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.
In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models.
We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python.
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).
The goal of this paper is to implement a system, titled as Drone Map Creator (DMC) using Computer Vision techniques.