On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages (2020.coling-main)
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| Challenge: | recurrent models have been effective in NLP tasks but performance on context-free languages (CFLs) is weak. |
| Approach: | They evaluate the performance of recurrent models on Dyck-n languages . they find that they are expressive enough to recognize Dyck words of arbitrary lengths if their depths are bounded. |
| Outcome: | The proposed models generalize well on Dyck-n languages, while performing poorly on longer test strings. |
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