Abstract
Chinese sentence semantic matching is a fundamental task in natural language processing, which aims to distinguish whether two Chinese sentences are semantically similar or not. Originated from English semantic matching task, most existing matching methods merely focus on learning the sentence representation from word granularity, but neglect the uniqueness of Chinese characters and the semantic interactions within a sentence on different granularities, and the interactions between sentences. As a result, most existing matching methods on Chinese language only achieve very limited performance improvement. In the paper, we propose a multi-perspective interactive (MPI) model for Chinese sentence semantic matching, which first employs a multi-granularity encoding layer to transform the characters and words in sentences into their embedding representation, then devises a multi-perspective interactive layer to capture the intra-sentence interactions within a sentence but on different granularities and the inter-sentence interactions between sentences. Finally, a prediction layer takes all the captured interactions as input to estimate the matching degree. We also conduct extensive experiments on real-world data set to assess the model performance. The extensive experimental results demonstrate that our proposed model achieves significantly better performance than the compared benchmarks.
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Notes
- 1.
Codes are available at https://github.com/baoshuo/MPI.
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Acknowledgment
The research work is partly supported by National Key R&D Program of China under Grant No.2018YFC0830705 and No.2018YFC0831700, National Natural Science Foundation of China under Grant No.61502259, and Key Program of Science and Technology of Shandong Province under Grant No.2020CXGC010901 and No.2019JZZY020124. Wenpeng Lu is the corresponding author.
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Kan, B., Lu, W., Li, F., Wu, H., Zhao, P., Zhang, X. (2021). Multi-Perspective Interactive Model for Chinese Sentence Semantic Matching. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_55
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