Work

Generative Approach for Gender-Rewriting Task with ArabicT5

Year: 2022

Type: conference-paper

Abstract: Addressing the correct gender in generative tasks (e.g., Machine Translation) has been an overlooked issue in the Arabic NLP.However, the recent introduction of the Arabic Parallel Gender Corpus (APGC) dataset has established new baselines for the Arabic Gender Rewriting task.To address the Gender Rewriting task, we first pre-train our new Seq2Seq Ara-bicT5 model on a 17GB of Arabic Corpora.Then, we continue pre-training our ArabicT5 model on the APGC dataset using a newly proposed method.Our evaluation shows that our ArabicT5 model, when trained on the APGC dataset, achieved competitive results against existing state-of-the-art methods.In addition, our ArabicT5 model shows better results on the APGC dataset compared to other Arabic and multilingual T5 models.

Source: Arabic Natural Language Processing Workshop (WANLP)

DOI: 10.18653/v1/2022.wanlp-1.55

Authors: Sultan Alrowili, K. Vijay-Shanker

Institution: University of Delaware

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Gender equality

Open Access status: hybrid

OpenAlex ID: W4385572989