@inproceedings{muti-etal-2022-leaningtower,
title = "{L}eaning{T}ower@{LT}-{EDI}-{ACL}2022: When Hope and Hate Collide",
author = "Muti, Arianna and
Marchiori Manerba, Marta and
Korre, Katerina and
Barr{\'o}n-Cede{\~n}o, Alberto",
editor = "Chakravarthi, Bharathi Raja and
Bharathi, B and
McCrae, John P and
Zarrouk, Manel and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ltedi-1.46",
doi = "10.18653/v1/2022.ltedi-1.46",
pages = "306--311",
abstract = "The 2022 edition of LT-EDI proposed two tasks in various languages. Task Hope Speech Detection required models for the automatic identification of hopeful comments for equality, diversity, and inclusion. Task Homophobia/Transphobia Detection focused on the identification of homophobic and transphobic comments. We targeted both tasks in English by using reinforced BERT-based approaches. Our core strategy aimed at exploiting the data available for each given task to augment the amount of supervised instances in the other. On the basis of an active learning process, we trained a model on the dataset for Task $i$ and applied it to the dataset for Task $j$ to iteratively integrate new silver data for Task $i$. Our official submissions to the shared task obtained a macro-averaged F$_1$ score of 0.53 for Hope Speech and 0.46 for Homo/Transphobia, placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.",
}
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<abstract>The 2022 edition of LT-EDI proposed two tasks in various languages. Task Hope Speech Detection required models for the automatic identification of hopeful comments for equality, diversity, and inclusion. Task Homophobia/Transphobia Detection focused on the identification of homophobic and transphobic comments. We targeted both tasks in English by using reinforced BERT-based approaches. Our core strategy aimed at exploiting the data available for each given task to augment the amount of supervised instances in the other. On the basis of an active learning process, we trained a model on the dataset for Task i and applied it to the dataset for Task j to iteratively integrate new silver data for Task i. Our official submissions to the shared task obtained a macro-averaged F₁ score of 0.53 for Hope Speech and 0.46 for Homo/Transphobia, placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.</abstract>
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%0 Conference Proceedings
%T LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide
%A Muti, Arianna
%A Marchiori Manerba, Marta
%A Korre, Katerina
%A Barrón-Cedeño, Alberto
%Y Chakravarthi, Bharathi Raja
%Y Bharathi, B.
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F muti-etal-2022-leaningtower
%X The 2022 edition of LT-EDI proposed two tasks in various languages. Task Hope Speech Detection required models for the automatic identification of hopeful comments for equality, diversity, and inclusion. Task Homophobia/Transphobia Detection focused on the identification of homophobic and transphobic comments. We targeted both tasks in English by using reinforced BERT-based approaches. Our core strategy aimed at exploiting the data available for each given task to augment the amount of supervised instances in the other. On the basis of an active learning process, we trained a model on the dataset for Task i and applied it to the dataset for Task j to iteratively integrate new silver data for Task i. Our official submissions to the shared task obtained a macro-averaged F₁ score of 0.53 for Hope Speech and 0.46 for Homo/Transphobia, placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.
%R 10.18653/v1/2022.ltedi-1.46
%U https://aclanthology.org/2022.ltedi-1.46
%U https://doi.org/10.18653/v1/2022.ltedi-1.46
%P 306-311
Markdown (Informal)
[LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide](https://aclanthology.org/2022.ltedi-1.46) (Muti et al., LTEDI 2022)
ACL
- Arianna Muti, Marta Marchiori Manerba, Katerina Korre, and Alberto Barrón-Cedeño. 2022. LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 306–311, Dublin, Ireland. Association for Computational Linguistics.