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Register a new userAutobots@LT-EDI-EACL2021: One World, One Family: Hope Speech Detection with BERT Transformer Model
Autobots @ LT-EDI-EACL2021: عالم واحد، عائلة واحدة: الكشف عن الكلام الأمل مع نموذج محول بيرت
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التراجع السريع للشبكات الاجتماعية عبر الإنترنت مثل YouTube، Facebook، Twitter يسمح للناس بالتعبير عن آرائهم على نطاق واسع على الإنترنت.ومع ذلك، في الوقت نفسه، يمكن أن يؤدي إلى زيادة في الصراع والكراهية بين المستهلكين في شكل حرية خطاب.لذلك، من الضروري اتخاذ طريقة تقوية إيجابية للبحث عن مشجعة، إيجابية، مساعدة، ومحتوى وسائل الإعلام الاجتماعية الداعمة.في هذه الورقة، نحن تصف نموذج برت محول من أجل الكشف عن الكلام عن الأمل للوصول والتنوع والاندماج، المقدمة لمهمة LT-ADI-2021 2. يحقق نموذجنا مرجحا معدل F1 مرجح من 0.93 على مجموعة الاختبار.
The rapid rise of online social networks like YouTube, Facebook, Twitter allows people to express their views more widely online. However, at the same time, it can lead to an increase in conflict and hatred among consumers in the form of freedom of speech. Therefore, it is essential to take a positive strengthening method to research on encouraging, positive, helping, and supportive social media content. In this paper, we describe a Transformer-based BERT model for Hope speech detection for equality, diversity, and inclusion, submitted for LT-EDI-2021 Task 2. Our model achieves a weighted averaged f1-score of 0.93 on the test set.
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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 this paper, we describe our approach towards utilizing pre-trained models for the task of hope speech detection. We participated in Task 2: Hope Speech Detection for Equality, Diversity and Inclusion at LT-EDI-2021 @ EACL2021. The goal of this tas
k is to predict the presence of hope speech, along with the presence of samples that do not belong to the same language in the dataset. We describe our approach to fine-tuning RoBERTa for Hope Speech detection in English and our approach to fine-tuning XLM-RoBERTa for Hope Speech detection in Tamil and Malayalam, two low resource Indic languages. We demonstrate the performance of our approach on classifying text into hope-speech, non-hope and not-language. Our approach ranked 1st in English (F1 = 0.93), 1st in Tamil (F1 = 0.61) and 3rd in Malayalam (F1 = 0.83).
Due to the development of modern computer technology and the increase in the number of online media users, we can see all kinds of posts and comments everywhere on the internet. Hope speech can not only inspire the creators but also make other viewer
s pleasant. It is necessary to effectively and automatically detect hope speech. This paper describes the approach of our team in the task of hope speech detection. We use the attention mechanism to adjust the weight of all the output layers of XLM-RoBERTa to make full use of the information extracted from each layer, and use the weighted sum of all the output layers to complete the classification task. And we use the Stratified-K-Fold method to enhance the training data set. We achieve a weighted average F1-score of 0.59, 0.84, and 0.92 for Tamil, Malayalam, and English language, ranked 3rd, 2nd, and 2nd.
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.
This paper mainly introduces the relevant content of the task Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021-EACL 2021''. A total of three language datasets were provided, and we chose the English dataset to complete this
task. The specific task objective is to classify the given speech into Hope speech', Not Hope speech', and Not in intended language'. In terms of method, we use fine-tuned ALBERT and K fold cross-validation to accomplish this task. In the end, we achieved a good result in the rank list of the task result, and the final F1 score was 0.93, tying for first place. However, we will continue to try to improve methods to get better results in future work.
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