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    <abstract>Neural network architectures have been augmented with differentiable stacks in order to introduce a bias toward learning hierarchy-sensitive regularities. It has, however, proven difficult to assess the degree to which such a bias is effective, as the operation of the differentiable stack is not always interpretable. In this paper, we attempt to detect the presence of latent representations of hierarchical structure through an exploration of the unsupervised learning of constituency structure. Using a technique due to Shen et al. (2018a,b), we extract syntactic trees from the pushing behavior of stack RNNs trained on language modeling and classification objectives. We find that our models produce parses that reflect natural language syntactic constituencies, demonstrating that stack RNNs do indeed infer linguistically relevant hierarchical structure.</abstract>
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%0 Conference Proceedings
%T Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing
%A Merrill, William
%A Khazan, Lenny
%A Amsel, Noah
%A Hao, Yiding
%A Mendelsohn, Simon
%A Frank, Robert
%Y Linzen, Tal
%Y Chrupała, Grzegorz
%Y Belinkov, Yonatan
%Y Hupkes, Dieuwke
%S Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F merrill-etal-2019-finding
%X Neural network architectures have been augmented with differentiable stacks in order to introduce a bias toward learning hierarchy-sensitive regularities. It has, however, proven difficult to assess the degree to which such a bias is effective, as the operation of the differentiable stack is not always interpretable. In this paper, we attempt to detect the presence of latent representations of hierarchical structure through an exploration of the unsupervised learning of constituency structure. Using a technique due to Shen et al. (2018a,b), we extract syntactic trees from the pushing behavior of stack RNNs trained on language modeling and classification objectives. We find that our models produce parses that reflect natural language syntactic constituencies, demonstrating that stack RNNs do indeed infer linguistically relevant hierarchical structure.
%R 10.18653/v1/W19-4823
%U https://aclanthology.org/W19-4823
%U https://doi.org/10.18653/v1/W19-4823
%P 224-232

Markdown (Informal)

[Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing](https://aclanthology.org/W19-4823) (Merrill et al., BlackboxNLP 2019)

• Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing (Merrill et al., BlackboxNLP 2019)

ACL

• William Merrill, Lenny Khazan, Noah Amsel, Yiding Hao, Simon Mendelsohn, and Robert Frank. 2019. Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing. In Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 224–232, Florence, Italy. Association for Computational Linguistics.