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Register a new userZYJ@LT-EDI-EACL2021:XLM-RoBERTa-Based Model with Attention for Hope Speech Detection
ZYJ @ LT-EDI-EACL2021: نموذج يعمل XLM-Roberta مع الاهتمام للكشف عن الكلام الأمل
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نظرا لتطوير تكنولوجيا الكمبيوتر الحديثة والزيادة في عدد مستخدمي الوسائط عبر الإنترنت، يمكننا رؤية جميع أنواع المشاركات والتعليقات في كل مكان على الإنترنت.الكلام الأمل لا يمكن أن تلهم فقط المبدعين ولكن أيضا جعل المشاهدين الآخرين ممتعة.من الضروري أن يكتشف خطاب الأمل بشكل فعال وتلقي تلقائيا.تصف هذه الورقة نهج فريقنا في مهمة الكشف عن الكلام الأمل.نحن نستخدم آلية الاهتمام لضبط وزن جميع طبقات الإخراج من XLM-Roberta للاستفادة الكاملة من المعلومات المستخرجة من كل طبقة، واستخدام المبلغ المرجح لجميع طبقات الإخراج لإكمال مهمة التصنيف.ونحن نستخدم طريقة طية K الطبقية لتعزيز مجموعة بيانات التدريب.نحقق متوسط درجة F1 مرجح من 0.59 و 0.84 و 0.92 من أجل التاميل والمالايالام واللغة الإنجليزية، المرتبة الثالثة والثانية والثانية.
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 viewers 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.
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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 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.
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.
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).
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