@inproceedings{parameswaran-etal-2021-berts,
title = "{BERT}{'}s The Word : Sarcasm Target Detection using {BERT}",
author = "Parameswaran, Pradeesh and
Trotman, Andrew and
Liesaputra, Veronica and
Eyers, David",
editor = "Rahimi, Afshin and
Lane, William and
Zuccon, Guido",
booktitle = "Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association",
month = dec,
year = "2021",
address = "Online",
publisher = "Australasian Language Technology Association",
url = "https://aclanthology.org/2021.alta-1.21",
pages = "185--191",
abstract = "In 2019, the Australasian Language Technology Association (ALTA) organised a shared task to detect the target of sarcastic comments posted on social media. However, there were no winners as it proved to be a difficult task. In this work, we revisit the task posted by ALTA by using transformers, specifically BERT, given the current success of the transformer-based model in various NLP tasks. We conducted our experiments on two BERT models (TD-BERT and BERT-AEN). We evaluated our model on the data set provided by ALTA (Reddit) and two additional data sets: {`}book snippets{'} and {`}Tweets{'}. Our results show that our proposed method achieves a 15.2{\%} improvement from the current state-of-the-art system on the Reddit data set and 4{\%} improvement on Tweets.",
}
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<abstract>In 2019, the Australasian Language Technology Association (ALTA) organised a shared task to detect the target of sarcastic comments posted on social media. However, there were no winners as it proved to be a difficult task. In this work, we revisit the task posted by ALTA by using transformers, specifically BERT, given the current success of the transformer-based model in various NLP tasks. We conducted our experiments on two BERT models (TD-BERT and BERT-AEN). We evaluated our model on the data set provided by ALTA (Reddit) and two additional data sets: ‘book snippets’ and ‘Tweets’. Our results show that our proposed method achieves a 15.2% improvement from the current state-of-the-art system on the Reddit data set and 4% improvement on Tweets.</abstract>
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%0 Conference Proceedings
%T BERT’s The Word : Sarcasm Target Detection using BERT
%A Parameswaran, Pradeesh
%A Trotman, Andrew
%A Liesaputra, Veronica
%A Eyers, David
%Y Rahimi, Afshin
%Y Lane, William
%Y Zuccon, Guido
%S Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association
%D 2021
%8 December
%I Australasian Language Technology Association
%C Online
%F parameswaran-etal-2021-berts
%X In 2019, the Australasian Language Technology Association (ALTA) organised a shared task to detect the target of sarcastic comments posted on social media. However, there were no winners as it proved to be a difficult task. In this work, we revisit the task posted by ALTA by using transformers, specifically BERT, given the current success of the transformer-based model in various NLP tasks. We conducted our experiments on two BERT models (TD-BERT and BERT-AEN). We evaluated our model on the data set provided by ALTA (Reddit) and two additional data sets: ‘book snippets’ and ‘Tweets’. Our results show that our proposed method achieves a 15.2% improvement from the current state-of-the-art system on the Reddit data set and 4% improvement on Tweets.
%U https://aclanthology.org/2021.alta-1.21
%P 185-191
Markdown (Informal)
[BERT’s The Word : Sarcasm Target Detection using BERT](https://aclanthology.org/2021.alta-1.21) (Parameswaran et al., ALTA 2021)
ACL
- Pradeesh Parameswaran, Andrew Trotman, Veronica Liesaputra, and David Eyers. 2021. BERT’s The Word : Sarcasm Target Detection using BERT. In Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association, pages 185–191, Online. Australasian Language Technology Association.