@inproceedings{nithiyananthan-etal-2025-tsd,
    title = "{TSD}: Towards Computational Processing of {T}amil Similes - A {T}amil Simile Dataset",
    author = "Nithiyananthan, Aathavan  and
      Raveendra, Jathushan  and
      Thayasivam, Uthayasanker",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth  and
      Rajiakodi, Saranya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Cn, Subalalitha  and
      Chinnappa, Dhivya",
    booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
    month = may,
    year = "2025",
    address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.dravidianlangtech-1.99/",
    pages = "573--579",
    ISBN = "979-8-89176-228-2",
    abstract = "A simile is a powerful figure of speech that makes a comparison between two different things via shared properties, often using words like ``like'' or ``as'' to create vivid imagery, convey emotions, and enhance understanding. However, computational research on similes is limited in low-resource languages like Tamil due to the lack of simile datasets. This work introduces a manually annotated Tamil Simile Dataset (TSD) comprising around 1.5k simile sentences drawn from various sources. Our data annotation guidelines ensure that all the simile sentences are annotated with the three components, namely tenor, vehicle, and context. We benchmark our dataset for simile interpretation and simile generation tasks using chosen pre-trained language models (PLMs) and present the results. Our findings highlight the challenges of simile tasks in Tamil, suggesting areas for further improvement. We believe that TSD will drive progress in computational simile processing for Tamil and other low-resource languages, further advancing simile related tasks in Natural Language Processing."
}