Abstract
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 Network (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.
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Yunnan Baiyao is a kind of healing spray.
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Acknowledgements
This research was partially supported by grants from the National Key Research and Development Program of China (No. 2016YFB1000904) and the National Natural Science Foundation of China (Grants No. 61727809, U1605251, 61572540, and 61751202).
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Zhang, K., Lv, G., Chen, E., Wu, L., Liu, Q., Philip Chen, C.L. (2019). Context-Aware Dual-Attention Network for Natural Language Inference. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_15
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