Human language understanding operates at multiple levels of granularity (e.g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined. However, existing deep models with stacked layers do not explicitly model any sort of hierarchical process. This paper proposes a recursive Transformer model based on differentiable CKY style binary trees to emulate the composition process. We extend the bidirectional language model pre-training objective to this architecture, attempting to predict each word given its left and right abstraction nodes. To scale up our approach, we also introduce an efficient pruned tree induction algorithm to enable encoding in just a linear number of composition steps. Experimental results on language modeling and unsupervised parsing show the effectiveness of our approach.
@article{arxiv.2107.00967,
title = {R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling},
author = {Xiang Hu and Haitao Mi and Zujie Wen and Yafang Wang and Yi Su and Jing Zheng and Gerard de Melo},
journal= {arXiv preprint arXiv:2107.00967},
year = {2022}
}