ArXiv Preprint
Modeling text-based time-series to make prediction about a future event or
outcome is an important task with a wide range of applications. The standard
approach is to train and test the model using the same input window, but this
approach neglects the data collected in longer input windows between the
prediction time and the final outcome, which are often available during
training. In this study, we propose to treat this neglected text as privileged
information available during training to enhance early prediction modeling
through knowledge distillation, presented as Learning using Privileged
tIme-sEries Text (LuPIET). We evaluate the method on clinical and social media
text, with four clinical prediction tasks based on clinical notes and two
mental health prediction tasks based on social media posts. Our results show
LuPIET is effective in enhancing text-based early predictions, though one may
need to consider choosing the appropriate text representation and windows for
privileged text to achieve optimal performance. Compared to two other methods
using transfer learning and mixed training, LuPIET offers more stable
improvements over the baseline, standard training. As far as we are concerned,
this is the first study to examine learning using privileged information for
time-series in the NLP context.
Jinghui Liu, Daniel Capurro, Anthony Nguyen, Karin Verspoor
2023-01-26
