TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media
Daniel Loureiro, Aminette D’Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa-Anke, Leonardo Neves, Francesco Barbieri, Jose Camacho-Collados
Correct Metadata for
Abstract
Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.- Anthology ID:
- 2022.coling-1.296
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3353–3359
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.296/
- DOI:
- Bibkey:
- Cite (ACL):
- Daniel Loureiro, Aminette D’Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa-Anke, Leonardo Neves, Francesco Barbieri, and Jose Camacho-Collados. 2022. TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3353–3359, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media (Loureiro et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.296.pdf
- Code
- cardiffnlp/tempowic
- Data
- WiC
Export citation
@inproceedings{loureiro-etal-2022-tempowic,
title = "{T}empo{W}i{C}: An Evaluation Benchmark for Detecting Meaning Shift in Social Media",
author = "Loureiro, Daniel and
D{'}Souza, Aminette and
Muhajab, Areej Nasser and
White, Isabella A. and
Wong, Gabriel and
Espinosa-Anke, Luis and
Neves, Leonardo and
Barbieri, Francesco and
Camacho-Collados, Jose",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.296/",
pages = "3353--3359",
abstract = "Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media."
}
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%0 Conference Proceedings %T TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media %A Loureiro, Daniel %A D’Souza, Aminette %A Muhajab, Areej Nasser %A White, Isabella A. %A Wong, Gabriel %A Espinosa-Anke, Luis %A Neves, Leonardo %A Barbieri, Francesco %A Camacho-Collados, Jose %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F loureiro-etal-2022-tempowic %X Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media. %U https://aclanthology.org/2022.coling-1.296/ %P 3353-3359
Markdown (Informal)
[TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media](https://aclanthology.org/2022.coling-1.296/) (Loureiro et al., COLING 2022)
- TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media (Loureiro et al., COLING 2022)
ACL
- Daniel Loureiro, Aminette D’Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa-Anke, Leonardo Neves, Francesco Barbieri, and Jose Camacho-Collados. 2022. TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3353–3359, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.