Natural Language Inference over Interaction Space

@article{Gong2017NaturalLI,
  title={Natural Language Inference over Interaction Space},
  author={Yichen Gong and Heng Luo and Jian Zhang},
  journal={ArXiv},
  year={2017},
  volume={abs/1709.04348}
}
Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis. We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space. We show that an interaction tensor (attention weight) contains semantic information to solve… CONTINUE READING

Figures, Tables, Results, and Topics from this paper.

Key Quantitative Results

  • Our approach, without using any recurrent structure, achieves the new state-of-the-art performance of 80.0%, exceeding current state-of-the-art performance by more than 5%.

Citations

Publications citing this paper.
SHOWING 1-10 OF 69 CITATIONS

Image-Enhanced Multi-level Sentence Representation Net for Natural Language Inference

  • 2018 IEEE International Conference on Data Mining (ICDM)
  • 2018
VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Adversarial Examples with Difficult Common Words for Paraphrase Identification

Zhouxing Shi, Minlie Huang, Ting Yao, Jingfang Xu
  • ArXiv
  • 2019
VIEW 6 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Deep contextualized word representations

VIEW 1 EXCERPT
CITES BACKGROUND
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2017
2019

CITATION STATISTICS

  • 17 Highly Influenced Citations

  • Averaged 23 Citations per year from 2017 through 2019

References

Publications referenced by this paper.
SHOWING 1-10 OF 51 REFERENCES

Densely Connected Convolutional Networks

Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Deep Residual Learning for Image Recognition

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2015
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL