  Question-answering (QA) data often encodes essential information in many
facets. This paper studies a natural question: Can we get supervision from QA
data for other tasks (typically, non-QA ones)? For example, {\em can we use
QAMR (Michael et al., 2017) to improve named entity recognition?} We suggest
that simply further pre-training BERT is often not the best option, and propose
the {\em question-answer driven sentence encoding (QuASE)} framework. QuASE
learns representations from QA data, using BERT or other state-of-the-art
contextual language models. In particular, we observe the need to distinguish
between two types of sentence encodings, depending on whether the target task
is a single- or multi-sentence input; in both cases, the resulting encoding is
shown to be an easy-to-use plugin for many downstream tasks. This work may
point out an alternative way to supervise NLP tasks.
