Simple Temporal Adaptation to Changing Label Sets: Hashtag Prediction via Dense KNN

Niloofar Mireshghallah, Nikolai Vogler, Junxian He, Omar Florez, Ahmed El-Kishky, Taylor Berg-Kirkpatrick


Abstract
User-generated social media data is constantly changing as new trends influence online discussion and personal information is deleted due to privacy concerns. However, traditional NLP models rely on fixed training datasets, which means they are unable to adapt to temporal change—both test distribution shift and deleted training data—without frequent, costly re-training. In this paper, we study temporal adaptation through the task of longitudinal hashtag prediction and propose a non-parametric dense retrieval technique, which does not require re-training, as a simple but effective solution. In experiments on a newly collected, publicly available, year-long Twitter dataset exhibiting temporal distribution shift, our method improves by 64% over the best static parametric baseline while avoiding costly gradient-based re-training. Our approach is also particularly well-suited to dynamically deleted user data in line with data privacy laws, with negligible computational cost/performance loss.
Anthology ID:
2023.emnlp-main.452
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7302–7311
Language:
URL:
https://aclanthology.org/2023.emnlp-main.452
DOI:
10.18653/v1/2023.emnlp-main.452
Bibkey:
Cite (ACL):
Niloofar Mireshghallah, Nikolai Vogler, Junxian He, Omar Florez, Ahmed El-Kishky, and Taylor Berg-Kirkpatrick. 2023. Simple Temporal Adaptation to Changing Label Sets: Hashtag Prediction via Dense KNN. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7302–7311, Singapore. Association for Computational Linguistics.
Cite (Informal):
Simple Temporal Adaptation to Changing Label Sets: Hashtag Prediction via Dense KNN (Mireshghallah et al., EMNLP 2023)
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https://preview.aclanthology.org/emnlp-22-attachments/2023.emnlp-main.452.pdf