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Register a new userHopeful NLP@LT-EDI-EACL2021: Finding Hope in YouTube Comment Section
الأمل NLP @ LT-EDI-EACL2021: العثور على أمل في قسم تعليق يوتيوب
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أصبح انتشار خطاب الكراهية والتضليل في وسائل التواصل الاجتماعي سريعا للخطر للمجتمع.في مجاملة، تصبح نشر الرسائل الواعدة والنشرة الواعدة وغير القمعية بديلا فريدا.لسوء الحظ، نظرا لطبيعتها المعقدة وكذلك المظهر المحدود نسبيا بالمقارنة مع المحتوى المعادي والمحايد، يصبح تحديد خطاب الأمل تحديا.يدور هذا العمل حول اكتشاف خطاب الأمل في تعليقات يوتيوب، للمهمة المشتركة على الكشف عن الكلام على الأمل للمساواة والتنوع والإدماج.نحن نحقق درجة F 0.93، ترتيب 1ST على المتصدرين للتعليقات الإنجليزية.
The proliferation of Hate Speech and misinformation in social media is fast becoming a menace to society. In compliment, the dissemination of hate-diffusing, promising and anti-oppressive messages become a unique alternative. Unfortunately, due to its complex nature as well as the relatively limited manifestation in comparison to hostile and neutral content, the identification of Hope Speech becomes a challenge. This work revolves around the detection of Hope Speech in Youtube comments, for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion. We achieve an f-score of 0.93, ranking 1st on the leaderboard for English comments.
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Language as a significant part of communication should be inclusive of equality and diversity. The internet user's language has a huge influence on peer users all over the world. People express their views through language on virtual platforms like F
acebook, Twitter, YouTube etc. People admire the success of others, pray for their well-being, and encourage on their failure. Such inspirational comments are hope speech comments. At the same time, a group of users promotes discrimination based on gender, racial, sexual orientation, persons with disability, and other minorities. The current paper aims to identify hope speech comments which are very important to move on in life. Various machine learning and deep learning based models (such as support vector machine, logistics regression, convolutional neural network, recurrent neural network) are employed to identify the hope speech in the given YouTube comments. The YouTube comments are available in English, Tamil and Malayalam languages and are part of the task EACL-2021:Hope Speech Detection for Equality, Diversity and Inclusion''.
This paper aims to describe the approach we used to detect hope speech in the HopeEDI dataset. We experimented with two approaches. In the first approach, we used contextual embeddings to train classifiers using logistic regression, random forest, SV
M, and LSTM based models. The second approach involved using a majority voting ensemble of 11 models which were obtained by fine-tuning pre-trained transformer models (BERT, ALBERT, RoBERTa, IndicBERT) after adding an output layer. We found that the second approach was superior for English, Tamil and Malayalam. Our solution got a weighted F1 score of 0.93, 0.75 and 0.49 for English, Malayalam and Tamil respectively. Our solution ranked 1st in English, 8th in Malayalam and 11th in Tamil.
In today's society, the rapid development of communication technology allows us to communicate with people from different parts of the world. In the process of communication, each person treats others differently. Some people are used to using offens
ive and sarcastic language to express their views. These words cause pain to others and make people feel down. Some people are used to sharing happiness with others and encouraging others. Such people bring joy and hope to others through their words. On social media platforms, these two kinds of language are all over the place. If people want to make the online world a better place, they will have to deal with both. So identifying offensive language and hope language is an essential task. There have been many assignments about offensive language. Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021-EACL 2021 uses another unique perspective -- to identify the language of Hope to make contributions to society. The XLM-Roberta model is an excellent multilingual model. Our team used a fine-tuned XLM-Roberta model to accomplish this task.
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.
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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