  Siamese networks have gained popularity as a method for modeling text
semantic similarity. Traditional methods rely on pooling operation to compress
the semantic representations from Transformer blocks in encoding, resulting in
two-dimensional semantic vectors and the loss of hierarchical semantic
information from Transformer blocks. Moreover, this limited structure of
semantic vectors is akin to a flattened landscape, which restricts the methods
that can be applied in downstream modeling, as they can only navigate this flat
terrain. To address this issue, we propose a novel 3D Siamese network for text
semantic similarity modeling, which maps semantic information to a
higher-dimensional space. The three-dimensional semantic tensors not only
retains more precise spatial and feature domain information but also provides
the necessary structural condition for comprehensive downstream modeling
strategies to capture them. Leveraging this structural advantage, we introduce
several modules to reinforce this 3D framework, focusing on three aspects:
feature extraction, attention, and feature fusion. Our extensive experiments on
four text semantic similarity benchmarks demonstrate the effectiveness and
efficiency of our 3D Siamese Network.
