End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions

Wenpeng Yin, Dan Roth, Hinrich Schütze


Abstract
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 ≈5% improvement over prior state of the art and that the pretrained DEISTE on SciTail generalizes well on RTE-5.
Anthology ID:
P18-2086
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
540–545
Language:
URL:
https://aclanthology.org/P18-2086
DOI:
10.18653/v1/P18-2086
Bibkey:
Cite (ACL):
Wenpeng Yin, Dan Roth, and Hinrich Schütze. 2018. End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 540–545, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions (Yin et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2086.pdf
Poster:
 P18-2086.Poster.pdf
Code
 yinwenpeng/SciTail
Data
SNLI