  Some questions have multiple answers that are not equally correct, i.e.
answers are different under different conditions. Conditions are used to
distinguish answers as well as to provide additional information to support
them. In this paper, we study a more challenging task where answers are
constrained by a list of conditions that logically interact, which requires
performing logical reasoning over the conditions to determine the correctness
of the answers. Even more challenging, we only provide evidences for a subset
of the conditions, so some questions may not have deterministic answers. In
such cases, models are asked to find probable answers and identify conditions
that need to be satisfied to make the answers correct. We propose a new model,
TReasoner, for this challenging reasoning task. TReasoner consists of an
entailment module, a reasoning module, and a generation module (if the answers
are free-form text spans). TReasoner achieves state-of-the-art performance on
two benchmark conditional QA datasets, outperforming the previous
state-of-the-art by 3-10 points.
