@inproceedings{upadhyay-etal-2021-hopeful,
title = "Hopeful Men@{LT}-{EDI}-{EACL}2021: Hope Speech Detection Using Indic Transliteration and Transformers",
author = "Upadhyay, Ishan Sanjeev and
E, Nikhil and
Wadhawan, Anshul and
Mamidi, Radhika",
booktitle = "Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.ltedi-1.23",
pages = "157--163",
abstract = "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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%0 Conference Proceedings
%T Hopeful Men@LT-EDI-EACL2021: Hope Speech Detection Using Indic Transliteration and Transformers
%A Upadhyay, Ishan Sanjeev
%A E, Nikhil
%A Wadhawan, Anshul
%A Mamidi, Radhika
%S Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Kyiv
%F upadhyay-etal-2021-hopeful
%X 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.
%U https://aclanthology.org/2021.ltedi-1.23
%P 157-163
Markdown (Informal)
[Hopeful Men@LT-EDI-EACL2021: Hope Speech Detection Using Indic Transliteration and Transformers](https://aclanthology.org/2021.ltedi-1.23) (Upadhyay et al., LTEDI 2021)
ACL