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Testing the Reasoning Power for NLI Models with Annotated Multi-perspective Entailment Dataset

  • Dong YuEmail author
  • Lu Liu
  • Chen Yu
  • Changliang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11856)

Abstract

Natural language inference (NLI) is a challenging task to determine the relationship between a pair of sentences. Existing Neural Network-based (NN-based) models have achieved prominent success. However, rare models are interpretable. In this paper, we propose a Multi-perspective Entailment Category Labeling System (METALs). It consists of three categories, ten sub-categories. We manually annotate 3,368 entailment items. The annotated data is used to explain the recognition ability of four NN-based models at a fine-grained level. The experimental results show that all the models have poor performance in the commonsense reasoning than in other entailment categories. The highest accuracy difference is 13.22%.

Keywords

Natural Language Inference Multi-perspective Entailment Category Labeling System Entailment categories 

Notes

Acknowledgments

This work is funded by National Key R&D Program of China, “Cloud computing and big data” key projects (2018YFB1005105).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Beijing Language and Culture UniversityBeijingChina
  2. 2.Kingsoft AI LabBeijingChina

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