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tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection
时间 2021-01-17
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tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection 文章发表在ACL2020。下面简单记录一下这篇文章的主要内容。 模型结构 如图1所示: 首先,用基础的BERT对两个句子 S 1 S_{1} S1, S 2 S_{2} S2进行编码。BERT最后一层的输出作为句子对的表示,用向量
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相关文章
1.
科恩论文阅读:Semantic-Aware Neural Networks for Binary Code Similarity Detection
2.
RCNN:Rich feature hierarchies for accurate object detection and semantic segmentation
3.
文本相似度:Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, NLI and QA
4.
【论文阅读 - AAAI 2020】Order Matters:Semantic-Aware Neural Networks for Binary Code Similarity Detection
5.
Multi-Label Transfer Learning for Semantic Similarity
6.
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7.
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8.
Bridging the Gap Between Relevance Matching and Semantic Matching for Short Text Similarity Modeling
9.
ACL2020论文整理 - 知乎
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