@inproceedings{ali-etal-2021-sipos-benchmark,
    title = "{S}i{POS}: A Benchmark Dataset for {S}indhi Part-of-Speech Tagging",
    author = "Ali, Wazir  and
      Xu, Zenglin  and
      Kumar, Jay",
    editor = "Djabri, Souhila  and
      Gimadi, Dinara  and
      Mihaylova, Tsvetomila  and
      Nikolova-Koleva, Ivelina",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2021",
    month = sep,
    year = "2021",
    address = "Online",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.ranlp-srw.4/",
    pages = "22--30",
    abstract = "In this paper, we introduce the SiPOS dataset for part-of-speech tagging in the low-resource Sindhi language with quality baselines. The dataset consists of more than 293K tokens annotated with sixteen universal part-of-speech categories. Two experienced native annotators annotated the SiPOS using the Doccano text annotation tool with an inter-annotation agreement of 0.872. We exploit the conditional random field, the popular bidirectional long-short-term memory neural model, and self-attention mechanism with various settings to evaluate the proposed dataset. Besides pre-trained GloVe and fastText representation, the character-level representations are incorporated to extract character-level information using the bidirectional long-short-term memory encoder. The high accuracy of 96.25{\%} is achieved with the task-specific joint word-level and character-level representations. The SiPOS dataset is likely to be a significant resource for the low-resource Sindhi language."
}