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.

本文主要介绍了一种基于 LuPIET 的时间序列模型来进行文本预测的方法，借助被忽略的特权信息以提高模型性能，并应用于临床和社交媒体文本的早期预测任务中，结果表明该模型的性能更稳定有效，是第一项在 NLP 领域使用特权信息进行时间序列建模的相关研究。