  We propose Beam Tree Recursive Cell (BT-Cell) - a backpropagation-friendly
framework to extend Recursive Neural Networks (RvNNs) with beam search for
latent structure induction. We further extend this framework by proposing a
relaxation of the hard top-k operators in beam search for better propagation of
gradient signals. We evaluate our proposed models in different
out-of-distribution splits in both synthetic and realistic data. Our
experiments show that BTCell achieves near-perfect performance on several
challenging structure-sensitive synthetic tasks like ListOps and logical
inference while maintaining comparable performance in realistic data against
other RvNN-based models. Additionally, we identify a previously unknown failure
case for neural models in generalization to unseen number of arguments in
ListOps. The code is available at:
https://github.com/JRC1995/BeamTreeRecursiveCells.
