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

Submission Type: Regular Short Paper

Submission Track: Efficient Methods for NLP

Submission Track 2: Information Retrieval and Text Mining

Keywords: Retrieval Based Models, Ethics, Deletion, Temporal Adaptation, non-parametric

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.

Submission Number: 4302