@inproceedings{chen-etal-2024-cher,
    title = "{C}her at {KSAA}-{CAD} 2024: Compressing Words and Definitions into the Same Space for {A}rabic Reverse Dictionary",
    author = "Chen, Pinzhen  and
      Zhao, Zheng  and
      Shao, Shun",
    editor = "Habash, Nizar  and
      Bouamor, Houda  and
      Eskander, Ramy  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Abdelali, Ahmed  and
      Touileb, Samia  and
      Hamed, Injy  and
      Onaizan, Yaser  and
      Alhafni, Bashar  and
      Antoun, Wissam  and
      Khalifa, Salam  and
      Haddad, Hatem  and
      Zitouni, Imed  and
      AlKhamissi, Badr  and
      Almatham, Rawan  and
      Mrini, Khalil",
    booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2024.arabicnlp-1.75/",
    doi = "10.18653/v1/2024.arabicnlp-1.75",
    pages = "686--691",
    abstract = "We present Team Cher`s submission to the ArabicNLP 2024 KSAA-CAD shared task on the reverse dictionary for Arabic{---}the retrieval of words using definitions as a query. Our approach is based on a multi-task learning framework that jointly learns reverse dictionary, definition generation, and reconstruction tasks. This work explores different tokenization strategies and compares retrieval performance for each embedding architecture. Evaluation using the KSAA-CAD benchmark demonstrates the effectiveness of our multi-task approach and provides insights into the reverse dictionary task for Arabic. It is worth highlighting that we achieve strong performance without using any external resources in addition to the provided training data."
}