This work deals with SciTail, a natural entailment challenge derived from a
multi-choice question answering problem. The premises and hypotheses in SciTail
were generated with no awareness of each other, and did not specifically aim at
the entailment task. This makes it more challenging than other entailment data
sets and more directly useful to the end-task -- question answering. We propose
DEISTE (deep explorations of inter-sentence interactions for textual
entailment) for this entailment task. Given word-to-word interactions between
the premise-hypothesis pair ($P$, $H$), DEISTE consists of: (i) a
parameter-dynamic convolution to make important words in $P$ and $H$ play a
dominant role in learnt representations; and (ii) a position-aware attentive
convolution to encode the representation and position information of the
aligned word pairs. Experiments show that DEISTE gets $\approx$5\% improvement
over prior state of the art and that the pretrained DEISTE on SciTail
generalizes well on RTE-5.

该研究针对 SciTail 自然蕴含挑战，提出了 DEISTE 方法，该方法通过卷积来探索句子之间的相互作用，提高文本蕴含的准确性，实验结果表现优于其他同类方法。