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Register a new userSimon @ LT-EDI-EACL2021: Detecting Hope Speech with BERT
سيمون @ LT-EDI-EACL2021: كشف خطاب الأمل مع بيرت
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في مجتمع اليوم، يتيح لنا التطوير السريع لتكنولوجيا الاتصالات التواصل مع أشخاص من أجزاء مختلفة من العالم. في عملية الاتصال، يعامل كل شخص الآخرين بشكل مختلف. يتم استخدام بعض الأشخاص في استخدام اللغة الهجومية والساخرة للتعبير عن آرائهم. هذه الكلمات تسبب الألم للآخرين وجعل الناس يشعرون بالأسفل. يتم استخدام بعض الأشخاص لتقاسم السعادة مع الآخرين وتشجيع الآخرين. هؤلاء الناس يجلبون الفرح والأمل في الآخرين من خلال كلماتهم. على منصات وسائل التواصل الاجتماعي، فإن هذين النوعين من اللغة هم في كل مكان. إذا كان الناس يرغبون في جعل العالم عبر الإنترنت مكانا أفضل، فسيتعين عليهم التعامل معهما. لذلك تحديد اللغة الهجومية ولغة الأمل مهمة أساسية. كانت هناك العديد من المهام حول اللغة المسيئة. تستخدم مهمة مشتركة على الكشف عن الكلام عن الأمل للمساواة والتنوع والإدماج في LT-EDI 2021-EACL 2021 وجهة نظر فريدة أخرى - لتحديد لغة الأمل في تقديم مساهمات في المجتمع. نموذج XLM-Roberta هو نموذج ممتاز متعدد اللغات. استخدم فريقنا نموذج XLM-Roberta ذو ضبط ضبطه لإنجاز هذه المهمة.
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 offensive 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.
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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 s
peech. 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.
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).
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.
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.
Hope is an essential aspect of mental health stability and recovery in every individual in this fast-changing world. Any tools and methods developed for detection, analysis, and generation of hope speech will be beneficial. In this paper, we propose
a model on hope-speech detection to automatically detect web content that may play a positive role in diffusing hostility on social media. We perform the experiments by taking advantage of pre-processing and transfer-learning models. We observed that the pre-trained multilingual-BERT model with convolution neural networks gave the best results. Our model ranked first, third, and fourth ranks on English, Malayalam-English, and Tamil-English code-mixed datasets.
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