Differentiable Tree Operations Promote Compositional Generalization
Paul Soulos Edward Hu Kate McCurdy Yunmo Chen Roland Fernandez Paul Smolensky Jianfeng Gao
In the context of structure-to-structure transformation tasks, learning
sequences of discrete symbolic operations poses significant challenges due to
their non-differentiability. To facilitate the learning of these symbolic
sequences, we introduce a differentiable tree interpreter that compiles
high-level symbolic tree operations into subsymbolic matrix operations on
tensors. We present a novel Differentiable Tree Machine (DTM) architecture that
integrates our interpreter with an external memory and an agent that learns to
sequentially select tree operations to execute the target transformation in an
end-to-end manner. With respect to out-of-distribution compositional
generalization on synthetic semantic parsing and language generation tasks, DTM
achieves 100% while existing baselines such as Transformer, Tree Transformer,
LSTM, and Tree2Tree LSTM achieve less than 30%. DTM remains highly
interpretable in addition to its perfect performance.