Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization
Table 4: Training and testing accuracy scores on the FOL inference task. Mean and standard deviation over five runs.
['[EMPTY]', 'train', 'test']
All recurrent models outperform the summing baseline. Even the simplest recurrent network, the SRN, achieves higher training and testing accuracy scores than the tree-shaped matrix model. The GRU and LSTM even beat the tensor model. The LSTM obtains slightly lower scores than the GRU, which is unexpected given its more complex design, but perhaps the current challenge does not require separate forget and input gates.