The SAMER Arabic Text Simplification Corpus

• URL: http://arxiv.org/abs/2404.18615v1
• Date: Mon, 29 Apr 2024 11:34:06 GMT
• Title: The SAMER Arabic Text Simplification Corpus
• Authors: Bashar Alhafni, Reem Hazim, Juan Piñeros Liberato, Muhamed Al Khalil, Nizar Habash,
• Abstract summary: SAMER Corpus is the first manually annotated Arabic parallel corpus for text simplification targeting school-aged learners. Our corpus comprises texts of 159K words selected from 15 publicly available Arabic fiction novels published between 1865 and 1955. Our corpus includes readability level annotations at both the document and word levels, as well as two simplified parallel versions for each text targeting learners at two different readability levels.
• Score: 9.369209124775043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the SAMER Corpus, the first manually annotated Arabic parallel corpus for text simplification targeting school-aged learners. Our corpus comprises texts of 159K words selected from 15 publicly available Arabic fiction novels most of which were published between 1865 and 1955. Our corpus includes readability level annotations at both the document and word levels, as well as two simplified parallel versions for each text targeting learners at two different readability levels. We describe the corpus selection process, and outline the guidelines we followed to create the annotations and ensure their quality. Our corpus is publicly available to support and encourage research on Arabic text simplification, Arabic automatic readability assessment, and the development of Arabic pedagogical language technologies.

Related papers

• Guidelines for Fine-grained Sentence-level Arabic Readability Annotation [9.261022921574318]
  The Balanced Arabic Readability Evaluation Corpus (BAREC) project is designed to address the need for comprehensive Arabic language resources aligned with diverse readability levels. Inspired by the Taha/Arabi21 readability reference, BAREC aims to provide a standardized reference for assessing sentence-level Arabic text readability across 19 distinct levels. This paper focuses on our meticulous annotation guidelines, demonstrated through the analysis of 10,631 sentences/phrases (113,651 words)
  arXiv  Detail & Related papers  (2024-10-11T09:59:46Z)
• Strategies for Arabic Readability Modeling [9.976720880041688]
  Automatic readability assessment is relevant to building NLP applications for education, content analysis, and accessibility. We present a set of experimental results on Arabic readability assessment using a diverse range of approaches.
  arXiv  Detail & Related papers  (2024-07-03T11:54:11Z)
• A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus [71.77214818319054]
  Natural language inference is a proxy for natural language understanding. There is no publicly available NLI corpus for the Romanian language. We introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs.
  arXiv  Detail & Related papers  (2024-05-20T08:41:15Z)
• ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus [8.96693684560691]
  ZAEBUC-Spoken is a multilingual multidialectal Arabic-English speech corpus. The corpus presents a challenging set for automatic speech recognition (ASR) We take inspiration from established sets of transcription guidelines to present a set of guidelines handling issues of conversational speech, code-switching and orthography of both languages.
  arXiv  Detail & Related papers  (2024-03-27T01:19:23Z)
• A Corpus for Sentence-level Subjectivity Detection on English News Articles [49.49218203204942]
  We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English without relying on language-specific tools, such as lexicons or machine translation.
  arXiv  Detail & Related papers  (2023-05-29T11:54:50Z)
• Beyond Arabic: Software for Perso-Arabic Script Manipulation [67.31374614549237]
  We provide a set of finite-state transducer (FST) components and corresponding utilities for manipulating the writing systems of languages that use the Perso-Arabic script. The library also provides simple FST-based romanization and transliteration.
  arXiv  Detail & Related papers  (2023-01-26T20:37:03Z)
• A Bilingual Parallel Corpus with Discourse Annotations [82.07304301996562]
  This paper describes BWB, a large parallel corpus first introduced in Jiang et al. (2022), along with an annotated test set. The BWB corpus consists of Chinese novels translated by experts into English, and the annotated test set is designed to probe the ability of machine translation systems to model various discourse phenomena.
  arXiv  Detail & Related papers  (2022-10-26T12:33:53Z)
• The Open corpus of the Veps and Karelian languages: overview and applications [52.77024349608834]
  The Open Corpus of the Veps and Karelian Languages (VepKar) is an extension of the Veps created in 2009. The VepKar corpus comprises texts in Karelian and Veps, multifunctional dictionaries linked to them, and software with an advanced system of search. Future plans include developing a speech module for working with audio recordings and a syntactic tagging module using morphological analysis outputs.
  arXiv  Detail & Related papers  (2022-06-08T13:05:50Z)
• A Novel Corpus of Discourse Structure in Humans and Computers [55.74664144248097]
  We present a novel corpus of 445 human- and computer-generated documents, comprising about 27,000 clauses. The corpus covers both formal and informal discourse, and contains documents generated using fine-tuned GPT-2.
  arXiv  Detail & Related papers  (2021-11-10T20:56:08Z)
• Automatic Arabic Dialect Identification Systems for Written Texts: A Survey [0.0]
  Arabic dialect identification is a specific task of natural language processing, aiming to automatically predict the Arabic dialect of a given text. In this paper, we present a comprehensive survey of Arabic dialect identification research in written texts. We review the traditional machine learning methods, deep learning architectures, and complex learning approaches to Arabic dialect identification.
  arXiv  Detail & Related papers  (2020-09-26T15:33:16Z)
• TArC: Incrementally and Semi-Automatically Collecting a Tunisian Arabish Corpus [3.8580784887142774]
  This article describes the constitution process of the first morpho-syntactically annotated Tunisian Arabish Corpus (TArC) Arabish, also known as Arabizi, is a spontaneous coding of Arabic dialects in Latin characters and arithmographs (numbers used as letters)
  arXiv  Detail & Related papers  (2020-03-20T22:29:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.