@inproceedings{tukhtina-etal-2022-hse,
title = "{HSE} at {T}empo{W}i{C}: Detecting Meaning Shift in Social Media with Diachronic Language Models",
author = "Tukhtina, Elizaveta and
Kashleva, Kseniia and
Vydrina, Svetlana",
editor = "Barbieri, Francesco and
Camacho-Collados, Jose and
Dhingra, Bhuwan and
Espinosa-Anke, Luis and
Gribovskaya, Elena and
Lazaridou, Angeliki and
Loureiro, Daniel and
Neves, Leonardo",
booktitle = "Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.evonlp-1.6",
doi = "10.18653/v1/2022.evonlp-1.6",
pages = "35--38",
abstract = "This paper describes our methods for temporal meaning shift detection, implemented during the TempoWiC shared task. We present two systems: with and without time span data usage. Our approaches are based on the language models fine-tuned for Twitter domain. Both systems outperformed all the competition{'}s baselines except TimeLMs-SIM. Our best submission achieved the macro-F1 score of 70.09{\%} and took the 7th place. This result was achieved by using diachronic language models from the TimeLMs project.",
}
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%0 Conference Proceedings
%T HSE at TempoWiC: Detecting Meaning Shift in Social Media with Diachronic Language Models
%A Tukhtina, Elizaveta
%A Kashleva, Kseniia
%A Vydrina, Svetlana
%Y Barbieri, Francesco
%Y Camacho-Collados, Jose
%Y Dhingra, Bhuwan
%Y Espinosa-Anke, Luis
%Y Gribovskaya, Elena
%Y Lazaridou, Angeliki
%Y Loureiro, Daniel
%Y Neves, Leonardo
%S Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F tukhtina-etal-2022-hse
%X This paper describes our methods for temporal meaning shift detection, implemented during the TempoWiC shared task. We present two systems: with and without time span data usage. Our approaches are based on the language models fine-tuned for Twitter domain. Both systems outperformed all the competition’s baselines except TimeLMs-SIM. Our best submission achieved the macro-F1 score of 70.09% and took the 7th place. This result was achieved by using diachronic language models from the TimeLMs project.
%R 10.18653/v1/2022.evonlp-1.6
%U https://aclanthology.org/2022.evonlp-1.6
%U https://doi.org/10.18653/v1/2022.evonlp-1.6
%P 35-38
Markdown (Informal)
[HSE at TempoWiC: Detecting Meaning Shift in Social Media with Diachronic Language Models](https://aclanthology.org/2022.evonlp-1.6) (Tukhtina et al., EvoNLP 2022)
ACL