  As a broad and major category in machine reading comprehension (MRC), the
generalized goal of discriminative MRC is answer prediction from the given
materials. However, the focuses of various discriminative MRC tasks may be
diverse enough: multi-choice MRC requires model to highlight and integrate all
potential critical evidence globally; while extractive MRC focuses on higher
local boundary preciseness for answer extraction. Among previous works, there
lacks a unified design with pertinence for the overall discriminative MRC
tasks. To fill in above gap, we propose a lightweight POS-Enhanced Iterative
Co-Attention Network (POI-Net) as the first attempt of unified modeling with
pertinence, to handle diverse discriminative MRC tasks synchronously. Nearly
without introducing more parameters, our lite unified design brings model
significant improvement with both encoder and decoder components. The
evaluation results on four discriminative MRC benchmarks consistently indicate
the general effectiveness and applicability of our model, and the code is
available at https://github.com/Yilin1111/poi-net.
