Figure4

Enhancing temporal commonsense understanding using disentangled attention-based method with a hybrid data framework

Figure 4. Overview of our model's processing pipeline for temporal commonsense reasoning. The input is first converted into embeddings and positional embeddings, which are then processed through the transformer layer. The output is passed to the RTD module with GDES, and the final hidden states are computed for reasoning tasks. RTD: Replaced token detection; GDES: gradient disentangled embedding sharing

Intelligence & Robotics
ISSN 2770-3541 (Online)
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