@inproceedings{yeghaneh-abkenar-stede-2024-neural,
    title = "Neural Mining of {P}ersian Short Argumentative Texts",
    author = "Yeghaneh Abkenar, Mohammad  and
      Stede, Manfred",
    editor = "Ojha, Atul Kr.  and
      Ahmadi, Sina  and
      Cinkov{\'a}, Silvie  and
      Fransen, Theodorus  and
      Liu, Chao-Hong  and
      McCrae, John P.",
    booktitle = "Proceedings of the 2nd Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia (EURALI) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.eurali-1.5",
    pages = "30--35",
    abstract = "Argumentation mining (AM) is concerned with extracting arguments from texts and classifying the elements (e.g.,claim and premise) and relations between them, as well as creating an argumentative structure. A significant hurdle to research in this area for the Persian language is the lack of annotated Persian language corpora. This paper introduces the first argument-annotated corpus in Persian and thereby the possibility of expanding argumentation mining to this low-resource language. The starting point is the English argumentative microtext corpus (AMT) (Peldszus and Stede, 2015), and we built the Persian variant by machine translation (MT) and careful post-editing of the output. We call this corpus Persian argumentative microtext (PAMT). Moreover, we present the first results for Argumentative Discourse Unit (ADU) classification for Persian, which is considered to be one of the main fundamental subtasks of argumentation mining. We adopted span categorization using the deep learning model of spaCy Version 3.0 (a CNN model on top of Bloom embedding with attention) on the corpus for determing argumentative units and their type (claim vs. premise).",
}

Argumentation mining (AM) is concerned with extracting arguments from texts and classifying the elements (e.g.,claim and premise) and relations between them, as well as creating an argumentative structure. A significant hurdle to research in this area for the Persian language is the lack of annotated Persian language corpora. This paper introduces the first argument-annotated corpus in Persian and thereby the possibility of expanding argumentation mining to this low-resource language. The starting point is the English argumentative microtext corpus (AMT) (Peldszus and Stede, 2015), and we built the Persian variant by machine translation (MT) and careful post-editing of the output. We call this corpus Persian argumentative microtext (PAMT). Moreover, we present the first results for Argumentative Discourse Unit (ADU) classification for Persian, which is considered to be one of the main fundamental subtasks of argumentation mining. We adopted span categorization using the deep learning model of spaCy Version 3.0 (a CNN model on top of Bloom embedding with attention) on the corpus for determing argumentative units and their type (claim vs. premise).
Yeghaneh Abkenar, Mohammad Stede, Manfred
Neural Mining of Persian Short Argumentative Texts