• 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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References

SHOWING 1-10 OF 33 REFERENCES
HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion
TLDR
A Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not is constructed.
IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always hope in Transformers
TLDR
This paper portrays the work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021- EACL 2021 and works with several transformer-based models to classify social media comments as hope speech or not hope speech in English, Malayalam, and Tamil languages.
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
TLDR
HateXplain is introduced, the first benchmark hate speech dataset covering multiple aspects of the issue, and it is observed that existing state-of-the-art models, which utilize the human rationales for training, perform better in re- ducing unintended bias towards target communities.
SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter
TLDR
The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter, and provides an analysis and discussion about the participant systems and the results they achieved in both subtasks.
Making Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation
TLDR
An easy and efficient method to extend existing sentence embedding models to new languages by using the original (monolingual) model to generate sentence embeddings for the source language and then training a new system on translated sentences to mimic the original model.
Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter
TLDR
This work proposes novel Deep Neural Network structures serving as effective feature extractors, and explores the usage of background information in the form of different word embeddings pre-trained from unlabelled corpora to address the very challenging nature of identifying hate speech on the social media.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
TLDR
A benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models, which favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks.
ETHOS: a multi-label hate speech detection dataset
TLDR
‘ETHOS’ (multi-labEl haTe speecH detectiOn dataSet), a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform, and the annotation protocol used to create this dataset is presented.
Transformers: State-of-the-Art Natural Language Processing
TLDR
Transformers is an open-source library that consists of carefully engineered state-of-the art Transformer architectures under a unified API and a curated collection of pretrained models made by and available for the community.
Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German
TLDR
This paper presents the HASOC track and its two parts, creating test collections for languages with few resources and English for comparison, and presents the tasks, the data and the main results.
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