{
    "paper_id": "2020",
    "header": {
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        "date_generated": "2023-01-19T08:06:17.142402Z"
    },
    "title": "",
    "authors": [],
    "year": "",
    "venue": null,
    "identifiers": {},
    "abstract": "",
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        "paper_id": "2020",
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        "abstract": [],
        "body_text": [
            {
                "text": "Given the success of the first, second, and third workshops on Open-Source Arabic Corpora and Corpora Processing Tools (OSACT) in LREC 2014, LREC 2016 and LREC 2018, the fourth workshop comes to encourage researchers and practitioners of Arabic language technologies, including computational linguistics (CL), natural language processing (NLP), and information retrieval (IR) to share and discuss their research efforts, corpora, and tools. The workshop gives special attention to Human Language Technologies based on AI/Machine Learning, which is one of LREC 2020 hot topics. In addition to the general topics of CL, NLP and IR, the workshop gives special emphasis to Offensive Language Detection shared task.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Preface",
                "sec_num": null
            },
            {
                "text": "OSACT4 had an acceptance rate of 50%, where we received 12 regular papers from which 6 papers were accepted, in addition to 11 shared task papers. We believe that the accepted papers are of high quality and present a mixture of interesting topics.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Preface",
                "sec_num": null
            },
            {
                "text": "This year, we introduced the Shared Task on Offensive Language Detection. The shared task attempts to detect such speech in the realm of Arabic social media in two Subtasks. In subtask A, SemEval 2020 Arabic offensive language dataset, which contains 10,000 tweets that were manually annotated for offensiveness was used. Offensive tweets contain explicit or implicit insults or attacks against other people, or inappropriate language. Subtask B targets the identification of Hate Speech. A hate speech tweet contains insults or threats targeting a group based on their nationality, ethnicity, gender, political or sport affiliation, religious belief, or other common characteristics. Subtasks A and B share the same data splits. Subtask B is more challenging than Subtask A as only 5% of the tweets are labeled as hate speech, while 20% of the tweets are labeled as offensive. The shared task attracted many teams from different countries in the Middle East, Europe and US. In all, 40 and 33 teams signed up to participate in Subtasks A and B; among them, 14 and 13 teams submitted their system outputs in the two subtasks respectively.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Preface",
                "sec_num": null
            },
            {
                "text": "We would like to thank everyone who in one way or another helped in making this workshop a success. Our special thanks go to the members of the program committee, who did an excellent job in reviewing the submitted papers, and to the LREC organizers. Last but not least, we would like to thank our authors and the workshop participants. ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Preface",
                "sec_num": null
            }
        ],
        "back_matter": [],
        "bib_entries": {
            "BIBREF0": {
                "ref_id": "b0",
                "title": "Multitask Learning for Arabic Offensive Language and Hate-Speech Detection Ibrahim Abu Farha and Walid Magdy Combining Character and Word Embeddings for the Detection of Offensive Language in Arabic Abdullah I. Alharbi and Mark Lee Multi-Task Learning using AraBert for Offensive Language Detection Marc Djandji, Fady Baly, wissam antoun and Hazem Hajj Leveraging Affective Bidirectional Transformers for Offensive Language Detection AbdelRahim Elmadany",
                "authors": [],
                "year": null,
                "venue": "Chiyu Zhang, Muhammad Abdul-Mageed and Azadeh Hashemi Quick and Simple Approach for Detecting Hate Speech in Arabic Tweets Abeer Abuzayed and Tamer Elsayed",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Multitask Learning for Arabic Offensive Language and Hate-Speech Detection Ibrahim Abu Farha and Walid Magdy Combining Character and Word Embeddings for the Detection of Offensive Lan- guage in Arabic Abdullah I. Alharbi and Mark Lee Multi-Task Learning using AraBert for Offensive Language Detection Marc Djandji, Fady Baly, wissam antoun and Hazem Hajj Leveraging Affective Bidirectional Transformers for Offensive Language Detection AbdelRahim Elmadany, Chiyu Zhang, Muhammad Abdul-Mageed and Azadeh Hashemi Quick and Simple Approach for Detecting Hate Speech in Arabic Tweets Abeer Abuzayed and Tamer Elsayed",
                "links": null
            }
        },
        "ref_entries": {
            "TABREF1": {
                "type_str": "table",
                "html": null,
                "num": null,
                "content": "<table/>",
                "text": "An Arabic Tweets Sentiment Analysis Dataset (ATSAD) using Distant Supervision and Self Training Kathrein Abu Kwaik, Stergios Chatzikyriakidis, Simon Dobnik, Motaz Saad and Richard Johansson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1"
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