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Found 47 papers, 5 papers with code
Hierarchical Aggregation of Dialectal Data for Arabic Dialect Identification
We test the value of such aggregation by building language models and using them in dialect identification.
Gender-Aware Reinflection using Linguistically Enhanced Neural Models
In this paper, we present an approach for sentence-level gender reinflection using linguistically enhanced sequence-to-sequence models.
The Shared Task on Gender Rewriting
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.
NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task
We describe findings of the third Nuanced Arabic Dialect Identification Shared Task (NADI 2022).
User-Centric Gender Rewriting
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 Arabic Parallel Gender Corpus 2.0: Extensions and Analyses
Much of the research on this issue has focused on mitigating gender bias in English NLP models and systems.
The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models
In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models.
NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task
This Shared Task includes four subtasks: country-level Modern Standard Arabic (MSA) identification (Subtask 1. 1), country-level dialect identification (Subtask 1. 2), province-level MSA identification (Subtask 2. 1), and province-level sub-dialect identification (Subtask 2. 2).
A Panoramic Survey of Natural Language Processing in the Arab World
The term natural language refers to any system of symbolic communication (spoken, signed or written) without intentional human planning and design.
NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task
The data for the shared task covers a total of 100 provinces from 21 Arab countries and are collected from the Twitter domain.
A Spelling Correction Corpus for Multiple Arabic Dialects
In this paper, we present the MADAR CODA Corpus, a collection of 10, 000 sentences from five Arabic city dialects (Beirut, Cairo, Doha, Rabat, and Tunis) represented in the Conventional Orthography for Dialectal Arabic (CODA) in parallel with their raw original form.
A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance
We present de-lexical segmentation, a linguistically motivated alternative to greedy or other unsupervised methods, requiring only minimal language specific input.
Automatic Gender Identification and Reinflection in Arabic
The impressive progress in many Natural Language Processing (NLP) applications has increased the awareness of some of the biases these NLP systems have with regards to gender identities.
The MADAR Shared Task on Arabic Fine-Grained Dialect Identification
In this paper, we present the results and findings of the MADAR Shared Task on Arabic Fine-Grained Dialect Identification.
ADIDA: Automatic Dialect Identification for Arabic
This demo paper describes ADIDA, a web-based system for automatic dialect identification for Arabic text.
MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction
In this paper, we introduce MADARi, a joint morphological annotation and spelling correction system for texts in Standard and Dialectal Arabic.
Fine-Grained Arabic Dialect Identification
Previous work on the problem of Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic (6-way classification).
Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic
We present the second ever evaluated Arabic dialect-to-dialect machine translation effort, and the first to leverage external resources beyond a small parallel corpus.
Using Ambiguity Detection to Streamline Linguistic Annotation
Arabic writing is typically underspecified for short vowels and other markups, referred to as diacritics.
Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings
Evaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges.
Building an Arabic Machine Translation Post-Edited Corpus: Guidelines and Annotation
We present our guidelines and annotation procedure to create a human corrected machine translated post-edited corpus for the Modern Standard Arabic.
Guidelines and Framework for a Large Scale Arabic Diacritized Corpus
This paper presents the annotation guidelines developed as part of an effort to create a large scale manually diacritized corpus for various Arabic text genres.
DALILA: The Dialectal Arabic Linguistic Learning Assistant
Dialectal Arabic (DA) poses serious challenges for Natural Language Processing (NLP).
A Multidialectal Parallel Corpus of Arabic
The daily spoken variety of Arabic is often termed the colloquial or dialect form of Arabic.
YouDACC: the Youtube Dialectal Arabic Comment Corpus
This paper presents YOUDACC, an automatically annotated large-scale multi-dialectal Arabic corpus collected from user comments on Youtube videos.
A contrastive review of paraphrase acquisition techniques
This paper addresses the issue of what approach should be used for building a corpus of sententential paraphrases depending on one's requirements.
