In the computational linguistics (CL), natural language processing (NLP), and information retrieval (IR) communities, Arabic is considered to be relatively resource-poor compared to English. This situation was thought to be the reason for the limited number of language resources -based studies in Arabic. However, the past few years witnessed the emergence of new considerably large and free classical and Modern Standard Arabic (MSA) as well as dialectical corpora and to a lesser extent Arabic processing tools.

This workshop follows the footsteps of previous editions of OSACT to provide a forum for researchers to share and discuss their ongoing work. This workshop is timely given the continued rise in research projects focusing on Arabic Language Resources. The sixth workshop comes to encourage researchers and practitioners of Arabic language technologies, including CL, NLP and IR to share and discuss their latest research efforts, corpora, and tools. The workshop will also give special attention to Large Language Models (LLMs) and Generative AI, which is a hot topic nowadays. In addition to the general topics of CL, NLP and IR, the workshop will give a special emphasis on two shared tasks, namely: Arabic LLMs Hallucination and Dialect to MSA Machine Translation.

Task 1: Arabic LLMs Hallucination (contact Hamdy Mubarak)

The Arabic LLM Hallucination Shared Task challenges participants to address and mitigate issues related to hallucinated text generated by Arabic language models. It aims to enhance the reliability and trustworthiness of these models by fostering innovation in detection methods and strategies for reducing hallucination. This collaborative effort is vital for improving Arabic language model applications in information retrieval and text generation.

For this task, we share a dataset of 10,000 Arabic factual claims generated by ChatGPT and GPT4 and judged manually for factuality, and linguistic correctness by a wide range of annotators. If applicable, annotators provided verification links.

We challenge participants to predict whether a claim is correct or not, and if it is incorrect, how can they rewrite the claim to fix its factual errors?

Link: https://sites.google.com/view/arabic-llms-hallucination

Task 2: Dialect to MSA Machine Translation (contact Kareem Darwish)

The Dialect to MSA (Modern Standard Arabic) Machine Translation Shared Task offers an opportunity for researchers and practitioners to tackle the intricate challenge of translating various Arabic dialects into Modern Standard Arabic. With the rich linguistic diversity across Arabic-speaking regions, this task aims to advance machine translation capabilities and bridge the gap between colloquial spoken Arabic and the formal written language. Participants will work on developing and refining translation models that can accurately and fluently convert dialectal Arabic text into MSA, making it a crucial initiative for improving communication and comprehension in the Arabic-speaking world.

The shared task will cover multiple dialects, namely: Gulf, Egyptian, Levantine, Iraqi, and Maghrebi. For each dialect, a set of 500 sentences written in both MSA and dialect will be provided for finetuning, and the testing will be done on a set of 500 blind sentences. The participants are free to use whatever resources at their disposal to train and finetune their systems.

Link: https://codalab.lisn.upsaclay.fr/competitions/17118

Language Resources:

• Pre-trained Arabic language models and their applications.
• Surveying and evaluating the design of available Arabic corpora, their associated and processing tools.
• Availing new annotated corpora for NLP and IR applications such as named entity recognition, machine translation, sentiment analysis, text classification, and language learning.
• Evaluating the use of crowdsourcing platforms for Arabic data annotation.
• Open source Arabic processing toolkits.

Tools and Technologies:

• Language education, e.g., L1 and L2.
• Language modeling and pre-trained models.
• Tokenization, normalization, word segmentation, morphological analysis, part-of-speech tagging, etc.
• Sentiment analysis, dialect identification, and text classification.
• Dialect translation.
• Fake news detection.
• Web and social media search and analytics.
• Issues in the design, construction, and use of Arabic LRs: text, speech, sign, gesture, image, in single or multimodal/multimedia data.
• Guidelines, standards, best practices, and models for LRs interoperability.
• Methodologies and tools for LRs construction and annotation.
• Methodologies and tools for extraction and acquisition of knowledge.
• Ontologies, terminology, and knowledge representation.
• LRs and Semantic Web (including Linked Data, Knowledge Graphs, etc.).

Issues in the design, construction and use of Arabic LRs:

• Guidelines, standards, best practices and models for LRs interoperability.
• Methodologies and tools for LRs construction and annotation.
• Methodologies and tools for extraction and acquisition of knowledge.
• Ontologies, terminology and knowledge representation.
• LRs and Semantic Web (including Linked Data, Knowledge Graphs, etc.).

OSACT6 invites high-quality submissions written in English. Submissions of two forms of papers will be considered:

1. Regular long papers – up to eight (8) pages maximum*, presenting substantial, original, completed, and unpublished work.
2. Short papers – up to four (4) pages*, describing a small focused contribution, negative results, system demonstrations, etc.

* Excluding any number of additional pages for references, ethical consideration, conflict-of-interest, as well as data, and code availability statements.

Upon acceptance, final versions of long papers will be given one additional page – up to nine (9) pages of content plus unlimited pages for acknowledgments and references – so that reviewers’ comments can be taken into account. Final versions of short papers may have up to five (5) pages, plus unlimited pages for acknowledgments and references. For both long and short papers, all figures and tables that are part of the main text must fit within these page limits.

Furthermore, appendices or supplementary material will also be allowed ONLY in the final, camera-ready version, but not during submission, as papers should be reviewed without the need to refer to any supplementary materials.

Linguistic examples, if any, should be presented in the original language but also glossed into English to allow accessibility for a broader audience.

Note that paper types are decisions made orthogonal to the eventual, final form of presentation (i.e., oral versus poster).

Submission due: March 1, 2024
Notification of acceptance: March 25, 2024
Camera-ready papers due: March 30, 2024
Workshop date: May 25, 2024

The language of the workshop is English and submissions should be with respect to LREC 2024 paper submission instructions (https://lrec-coling-2024.org/authors-kit/). All papers will be peer reviewed, possibly by three independent referees. Papers must be submitted electronically in PDF format to the START system.

When submitting a paper from the START page, authors will be asked to provide essential information about resources (in a broad sense, i.e. also technologies, standards, evaluation kits, etc.) that have been used for the work described in the paper or are a new result of your research.

Moreover, ELRA encourages all LREC authors to share the described LRs (data, tools, services, etc.) to enable their reuse and replicability of experiments (including evaluation ones).
Submission Link: https://softconf.com/lrec-coling2024/osact2024/