Browsing by Author "Priyadharshini, R."

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    Findings of the Shared Task on Machine Translation in Dravidian languages
    (Association for Computational Linguistics (ACL), 2021) Chakravarthi, B.R.; Priyadharshini, R.; Banerjee, S.; Saldanha, R.; Mccrae, J.P.; Anand Kumar, M.; Krishnamurthy, P.; Johnson, M.
    This paper presents an overview of the shared task on machine translation of Dravidian languages. We presented the shared task results at the EACL 2021 workshop on Speech and Language Technologies for Dravidian Languages. This paper describes the datasets used, the methodology used for the evaluation of participants, and the experiments’ overall results. As a part of this shared task, we organized four sub-tasks corresponding to machine translation of the following language pairs: English to Tamil, English to Malayalam, English to Telugu and Tamil to Telugu which are available at https://competitions.codalab.org/competitions/27650. We provided the participants with training and development datasets to perform experiments, and the results were evaluated on unseen test data. In total, 46 research groups participated in the shared task and 7 experimental runs were submitted for evaluation. We used BLEU scores for assessment of the translations. ©2021 Association for Computational Linguistics
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    Findings of the Shared Task on Offensive Language Identification in Tamil, Malayalam, and Kannada
    (Association for Computational Linguistics (ACL), 2021) Chakravarthi, B.R.; Priyadharshini, R.; Jose, N.; Anand Kumar, M.; Mandl, T.; Kumaresan, P.K.; Ponnusamy, R.; LekshmiAmmal, R.L.; Mccrae, J.P.; Sherly, E.
    Detecting offensive language in social media in local languages is critical for moderating user-generated content. Thus, the field of offensive language identification for under-resourced languages like Tamil, Malayalam and Kannada is of essential importance. As user-generated content is often code-mixed and not well studied for under-resourced languages, it is imperative to create resources and conduct benchmark studies to encourage research in under-resourced Dravidian languages. We created a shared task on offensive language detection in Dravidian languages. We summarize the dataset for this challenge which are openly available at https://competitions.codalab.org/competitions/27654, and present an overview of the methods and the results of the competing systems. ©2021 Association for Computational Linguistics