【摘 要】
：
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,rar
【机 构】
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Beijing Language and Culture University,Beijing,China
【出 处】
：
第十八届中国计算语言学大会暨中国中文信息学会2019学术年会
论文部分内容阅读
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%.
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