Search Results for author: Bashar Alhafni

Found 16 papers, 7 papers with code

Gender-Aware Reinflection using Linguistically Enhanced Neural Models

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.

Grammatical Error Correction Sentence

mEdIT: Multilingual Text Editing via Instruction Tuning

no code implementations26 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.

Grammatical Error Correction Text Simplification

Personalized Text Generation with Fine-Grained Linguistic Control

1 code implementation7 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.

Text Generation

Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation

1 code implementation24 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.

Grammatical Error Detection

The Shared Task on Gender Rewriting

no code implementations22 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.

Sentence

The User-Aware Arabic Gender Rewriter

no code implementations14 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.

Zero-shot Cross-Linguistic Learning of Event Semantics

no code implementations5 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.

User-Centric Gender Rewriting

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.

The Interplay of Variant, Size, and Task Type in Arabic Pre-trained 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.

Language Modelling

Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding

no code implementations26 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).

Relation Temporal Relation Extraction +1

Mapping Areas using Computer Vision Algorithms and Drones

no code implementations1 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.

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