Lightweight Text Matching Method
DOI:
https://doi.org/10.54097/ajst.v3i2.2171Keywords:
Text matching, NLP, Deep learning.Abstract
This paper constructs a lightweight text matching model. This model fully extracts the features of two text through the fusion of double tower and interactive methods, and then conducts deep interaction. Then, it classifies the last extracted text features through the classification network, and conducts training, validation and testing on the Chinese text matching dataset LCQMC. Although the experimental results show that the model in this paper is relatively good, it still needs to be improved.
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References
Pang Liang, Lan Yanyan, Xu Jun, Guo Jiafeng, Wan Shengxian, Cheng Xueqi. Overview of Deep Text Matching [J]. Journal of Computer Science, 2017,40 (04): 985-1003.
Huang P S, He X, Gao J, et al. Learning deep structured semantic models for web search using clickthrough data[C]//Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2013: 2333-2338.
Shen Y, He X, Gao J, et al. A latent semantic model with convolutional-pooling structure for information retrieval[C]//Proceedings of the 23rd ACM international conference on conference on information and knowledge management. 2014: 101-110.
Palangi H, Deng L, Shen Y, et al. Semantic modelling with long-short-term memory for information retrieval[J]. arXiv preprint arXiv:1412.6629, 2014.
Pang L, Lan Y, Guo J, et al. Text matching as image recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2016, 30(1).
Chen Q, Zhu X, Ling Z, et al. Enhanced LSTM for natural language inference[J]. arXiv preprint arXiv:1609.06038, 2016.
Wang Z, Hamza W, Florian R. Bilateral multi-perspective matching for natural language sentences[J]. arXiv preprint arXiv:1702.03814, 2017.
Yang Runqi, Zhang Jianhai, Gao Xing, Ji Feng, Chen Haiqing. (2019). Simple and Effective Text Matching with Richer Alignment Features. 4699-4709. 10.18653/v1/P19-1465.
Devlin J, Chang M W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018.
Lcqmc:A large-scale chinese question matching corpus[C]//Proceedings of the 27th International Conference on Computational Linguistics. 2018: 1952-1962.
Cui Y, Che W, Liu T, et al. Pre-training with whole word masking for chinese bert[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 3504-3514.
X. Zhang, F. Lei and S. Yu, "Self-attention Based Text Matching Model with Generative Pre-training," 2021, pp. 84-91,doi:10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00027.
Zhen Wang, Xiangxie Zhang, Yicong Tan. Chinese Sentences Similarity via Cross-Attention Based Siamese Network.2021, https://doi.org/10.48550/arXiv.2104.08787
