• Corpus ID: 233365095

TeamUNCC@LT-EDI-EACL2021: Hope Speech Detection using Transfer Learning with Transformers

@inproceedings{Mahajan2021TeamUNCCLTEDIEACL2021HS,
  title={TeamUNCC@LT-EDI-EACL2021: Hope Speech Detection using Transfer Learning with Transformers},
  author={Khyati Mahajan and Erfan Al-Hossami and Samira Shaikh},
  booktitle={LTEDI},
  year={2021}
}
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 task 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… 
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Several deep learning based models such as DNN (dense or fully connected neural network), CNN (Convolutional Neural Network), Bi-LSTM (Bidirectional Long Short Term Memory Network), and GRU(Gated Recurrent Unit) were employed to identify the hopeful comments in English-language YouTube comments.
LPS@LT-EDI-ACL2022:An Ensemble Approach about Hope Speech Detection
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Overlapping Word Removal is All You Need: Revisiting Data Imbalance in Hope Speech Detection
TLDR
It is shown that introducing focal loss as part of Multilingual-BerT’s (M-BERT) training process mitigates the ef- fect of class imbalance and improves overall F1-Macro by 0.11, and that overlapping word removal based on pre-processing, though simple, improves F 1- Macro by0.28.
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TLDR
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TLDR
A multilingual hope speech dataset that promotes equality, diversity and inclusion (EDI) in English, Tamil, Malayalam and Kannada is presented that contains data collected from the LGBTQIA+ community, persons with disabilities and women in science, engineering, technology and management (STEM).
Hope speech detection in YouTube comments
TLDR
This work created a multilingual dataset to recognize and encourage positivity in the comments, and proposes a novel custom deep network architecture, which uses a concatenation of embedding from T5-Sentence.
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Without a sense of belonging, students may become disheartened and give up when faced with new challenges. Moreover, with the sudden growth of remote learning due to COVID-19, it may be even more…
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Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion
TLDR
An overview of the findings and results from the shared task on hope speech detection for Tamil, Malayalam, Kannada, English and Spanish languages conducted in the second workshop on Language Technology for Equality, Diversity and Inclusion organised as a part of ACL 2022 is reported.

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