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Register a new userFindings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion
نتائج المهمة المشتركة على الكشف عن الكلام الأمل للمساواة والتنوع والإدماج
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تعتبر الأمل مهمة بالنسبة لرفاهية الحياة البشرية واستعادتها من قبل المهنيين الصحيين.يعكس خطاب الأمل الاعتقاد بأن المرء يمكنه اكتشاف مسارات لأهدافها المرجوة وتصبح طرحا للاستفادة من هذه المسارات.لتشجيع البحث في معالجة اللغة الطبيعية نحو نهج التعزيز الإيجابي، أنشأنا مجموعة بيانات الكشف عن الكلام الأمل.هذه التقارير الورقية عن المهمة المشتركة الخاصة باكتشاف الكلام للأمل للتاميل والإنجليزية واللغات المالايالامية.أجريت المهمة المشتركة كجزء من ورشة عمل EACL 2021 حول تكنولوجيا اللغة للمساواة والتنوع والإدماج (LT-EDI-2021).نحن تلخص هنا مجموعات البيانات الخاصة بهذا التحدي المتوفرة بصراحة في https://competitions.codalab.org/competitions/27653، وتقديم نظرة عامة على الأساليب ونتائج النظم المنافسة.إلى حد ما من معرفتنا، هذه هي المهمة المشتركة الأولى لإجراء الكشف عن الكلام الأمل.
Hope is considered significant for the well-being, recuperation and restoration of human life by health professionals. Hope speech reflects the belief that one can discover pathways to their desired objectives and become roused to utilise those pathways. To encourage research in natural language processing towards positive reinforcement approach, we created a hope speech detection dataset. This paper reports on the shared task of hope speech detection for Tamil, English, and Malayalam languages. The shared task was conducted as a part of the EACL 2021 workshop on Language Technology for Equality, Diversity, and Inclusion (LT-EDI-2021). We summarize here the datasets for this challenge which are openly available at https://competitions.codalab.org/competitions/27653, and present an overview of the methods and the results of the competing systems. To the best of our knowledge, this is the first shared task to conduct hope speech detection.
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This paper describes the IIITK's team submissions to the hope speech detection for equality, diversity and inclusion in Dravidian languages shared task organized by LT-EDI 2021 workshop@EACL 2021. Our best configurations for the shared tasks achieve
weighted F1 scores of 0.60 for Tamil, 0.83 for Malayalam, and 0.93 for English. We have secured ranks of 4, 3, 2 in Tamil, Malayalam and English respectively.
With the internet becoming part and parcel of our lives, engagement in social media has increased a lot. Identifying and eliminating offensive content from social media has become of utmost priority to prevent any kind of violence. However, detecting
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Analysis and deciphering code-mixed data is imperative in academia and industry, in a multilingual country like India, in order to solve problems apropos Natural Language Processing. This paper proposes a bidirectional long short-term memory (BiLSTM)
with the attention-based approach, in solving the hope speech detection problem. Using this approach an F1-score of 0.73 (9thrank) in the Malayalam-English data set was achieved from a total of 31 teams who participated in the competition.
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