End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions
This work addresses a natural language understanding problem for researchers and practitioners in NLP, offering an incremental improvement in entailment tasks.
The authors tackled the challenging SciTail textual entailment dataset, which is derived from question answering and lacks direct entailment design, by proposing DEISTE, a method that improves state-of-the-art performance by approximately 5% and generalizes well to other datasets like RTE-5.
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.