Context-Aware Dual-Attention Network for Natural Language Inference
Natural Language Inference(NLI)is a fundamental task in natural language understanding.In spite of the importance of existing research on NLI,the problem of how to exploit the contexts of sentences for more precisely capturing the inference relations(i.e.by addressing the issues such as polysemy and ambiguity)is still much open.In this paper,we introduce the corresponding image into inference process.Along this line,we design a novel Context-Aware Dual-Attention Net-work(CADAN)for tackling NLI task.To be specific,we first utilize the corresponding images as the Image Attention to construct an enriched representation for sentences.Then,we use the enriched representation as the Sentence Attention to analyze the inference relations from detailed perspectives.Finally,a sentence matching method is designed to determine the inference relation in sentence pairs.Experimental results on large-scale NLI corpora and real-world NLI alike corpus demonstrate the superior effectiveness of our CADAN model.
Kun Zhang Guangyi Lv Enhong Chen Le Wu Qi Liu C.L.Philip Chen
Anhui Province Key Laboratory of Big Data Analysis and Application,School of Computer Science and Te Hefei University of Technology,Hefei,China University of Macau,Macau,China
国际会议
澳门
英文
185-198
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）