The paper introduces two novel attention mechanisms, Feature Attention (FA) and Selective Feature Attention (SFA), to enhance representation-based Siamese text matching networks.
The key highlights are:
The FA block employs a "squeeze-and-excitation" approach to dynamically adjust the emphasis on individual embedding features, enabling the network to concentrate more on features that significantly contribute to the final classification.
The SFA block builds upon the FA block and incorporates a dynamic "selection" mechanism based on a stacked BiGRU Inception structure. This allows the network to selectively focus on semantic information and embedding features across varying levels of abstraction.
The FA and SFA blocks offer a plug-and-play characteristic, allowing seamless integration with various Siamese networks.
Extensive experiments across diverse text matching baselines and benchmarks demonstrate the superiority of the "selection" mechanism in the SFA block, significantly improving inference accuracy compared to the baseline Siamese networks.
The authors analyze the impact of the "selection" mechanism on the gradient flow during training, showing how it leads to more efficient and stable training compared to the traditional Inception structure.
The authors explore different Inception network architectures, including CNN, RNN, and Transformer-based variants, and find that the stacked BiGRU Inception structure provides the best balance between performance and computational cost.
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by Jianxiang Za... às arxiv.org 04-26-2024
https://arxiv.org/pdf/2404.16776.pdfPerguntas Mais Profundas