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Register a new userIIITK@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion in Tamil , Malayalam and English
IIITK @ LT-EDI-EACL2021: الكشف عن الكلام للأمل للمساواة والتنوع والإدماج في التاميل والللايالام والإنجليزية
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توضح هذه الورقة تقديم فريق IIITK إلى اكتشاف الكلام في الأمل للهاتف والتنوع والإدراج في المهمة المشتركة لغات Dravidian التي نظمتها LT-EDI 2021 ورشة عمل @ EACL 2021. أفضل تكوينات المهام المشتركة تحقق درجات F1 المرجح من 0.60 من أجل التاميل0.83 للمالايالام، و 0.93 للغة الإنجليزية.لقد حصلنا على صفوف من 4 و 3 و 2 في التاميل المالايالام والإنجليزية على التوالي.
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.
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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
encouraging, supportive and positive content is equally important to prevent misuse of censorship targeted to attack freedom of speech. This paper presents our system for the shared task Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI, EACL 2021. The data for this shared task is provided in English, Tamil, and Malayalam which was collected from YouTube comments. It is a multiclass classification problem where each data instance is categorized into one of the three classes: Hope speech', Not hope speech', and Not in intended language'. We propose a system that employs multilingual transformer models to obtain the representation of text and classifies it into one of the three classes. We explored the use of multilingual models trained specifically for Indian languages along with generic multilingual models. Our system was ranked 2nd for English, 2nd for Malayalam, and 7th for the Tamil language in the final leader board published by organizers and obtained a weighted F1-score of 0.92, 0.84, 0.55 respectively on the hidden test dataset used for the competition. We have made our system publicly available at GitHub.
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 pathw
ays. 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.
This paper describes approaches to identify Hope Speech in short, informal texts in English, Malayalam and Tamil using different machine learning techniques. We demonstrate that even very simple baseline algorithms perform reasonably well on this tas
k if provided with enough training data. However, our best performing algorithm is a cross-lingual transfer learning approach in which we fine-tune XLM-RoBERTa.
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.
In a world with serious challenges like climate change, religious and political conflicts, global pandemics, terrorism, and racial discrimination, an internet full of hate speech, abusive and offensive content is the last thing we desire for. In this
paper, we work to identify and promote positive and supportive content on these platforms. We work with several transformer-based models to classify social media comments as hope speech or not hope speech in English, Malayalam, and Tamil languages. This paper portrays our work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021- EACL 2021. The codes for our best submission can be viewed.
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