Understand
In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods.
- In this paper, we introduce the MatchZoo toolkit that aims to facilitate the designing, comparing and sharing of deep text matching models.
- Specifically, the toolkit provides a unified data preparation module for different text matching problems, a flexible layer-based model construction process, and a variety of training objectives and evaluation metrics.
- In addition, the toolkit has implemented two schools of representative deep text matching models, namely representation-focused models and interaction-focused models.
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
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Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, Liang Pang, and Xueqi Cheng. 2016a
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Text Matching as Image Recognition. In AAAI
Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2016 · 2016
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Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN
Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, Liang Pang, and Xueqi Cheng. 2016b · 2016
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Convolutional neural network architectures for matching natural language sentences. In NIPS
Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. 2014 · 2050
Closest in time.
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