On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages

Satwik Bhattamishra, Kabir Ahuja, Navin Goyal

Abstract
While recurrent models have been effective in NLP tasks, their performance on context-free languages (CFLs) has been found to be quite weak. Given that CFLs are believed to capture important phenomena such as hierarchical structure in natural languages, this discrepancy in performance calls for an explanation. We study the performance of recurrent models on Dyck-n languages, a particularly important and well-studied class of CFLs. We find that while recurrent models generalize nearly perfectly if the lengths of the training and test strings are from the same range, they perform poorly if the test strings are longer. At the same time, we observe that RNNs are expressive enough to recognize Dyck words of arbitrary lengths in finite precision if their depths are bounded. Hence, we evaluate our models on samples generated from Dyck languages with bounded depth and find that they are indeed able to generalize to much higher lengths. Since natural language datasets have nested dependencies of bounded depth, this may help explain why they perform well in modeling hierarchical dependencies in natural language data despite prior works indicating poor generalization performance on Dyck languages. We perform probing studies to support our results and provide comparisons with Transformers.
Anthology ID:
2020.coling-main.129
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1481–1494
Language:
URL:
https://aclanthology.org/2020.coling-main.129
DOI:
10.18653/v1/2020.coling-main.129
Bibkey:
Cite (ACL):
Satwik Bhattamishra, Kabir Ahuja, and Navin Goyal. 2020. On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1481–1494, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages (Bhattamishra et al., COLING 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.129.pdf
Code
 satwik77/RNNs-Context-Free
PDF Search Code