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 first in English, eighth in Malayalam and eleventh in Tamil.

中文翻译：

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Hopeful_Men @ LT-EDI-EACL2021：使用印度音译和变形金刚的希望语音检测

本文旨在描述我们在HopeEDI数据集中检测希望语音的方法。我们尝试了两种方法。在第一种方法中，我们使用上下文嵌入来使用基于Logistic回归，随机森林，SVM和LSTM的模型来训练分类器。第二种方法涉及使用通过微调预训练的变压器模型而获得的11个模型的多数投票合奏（BERT，ALBERT，RoBERTa，IndicBERT）添加输出层之后。我们发现第二种方法在英语，泰米尔语和马拉雅拉姆语方面更胜一筹。对于英语，马拉雅拉姆语和泰米尔语，我们的解决方案的加权F1分数分别为0.93、0.75和0.49。我们的解决方案在英语中排名第一，在马拉雅拉姆语中排名第八，在泰米尔语中排名第十一位。