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Register a new userHopeful Men@LT-EDI-EACL2021: Hope Speech Detection Using Indic Transliteration and Transformers
متفائل الرجال @ LT-EDI-EACL2021: الكشف عن الكلام الأمل باستخدام نظام الترجمة والمحولات
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تهدف هذه الورقة إلى وصف النهج الذي اعتدنا عليه اكتشاف خطاب الأمل في مجموعة بيانات Hopeiedi.جربنا مع نهجين.في النهج الأول، استخدمنا ادارة سياقية لتدريب المصنفات باستخدام الانحدار اللوجستي، والغابات العشوائية، و SVM، و LSTM.النهج الثاني المعني باستخدام فرقة التصويت للأغلبية من 11 نماذج تم الحصول عليها عن طريق نماذج محولات محول مدربة مسبقا (بيرت، ألبرت، روبرتا، Inderbert) بعد إضافة طبقة إخراج.وجدنا أن النهج الثاني كان متفوقا على اللغة الإنجليزية والتاميل والمالايالامية.حصل حلنا على درجة مرجحة F1 من 0.93 و 0.75 و 0.49 للغة الإنجليزية ومالايالامية والتاميل على التوالي.احتل محلولنا في المرتبة الأولى باللغة الإنجليزية، الثامن في ملايال و 11 في التاميل.
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, SVM, 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.
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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.
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.
In this paper we work with a hope speech detection corpora that includes English, Tamil, and Malayalam datasets. We present a two phase mechanism to detect hope speech. In the first phase we build a classifier to identify the language of the text. In
the second phase, we build a classifier to detect hope speech, non hope speech, or not lang labels. Experimental results show that hope speech detection is challenging and there is scope for improvement.
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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