| Challenge: | Existing studies show that syntactic information is useful for a wide variety of NLP tasks. |
| Approach: | They propose to use word-level representations to learn internal representations that capture soft hierarchical notions of syntax from highly varied supervision. |
| Outcome: | The proposed model encodes significant amounts of syntax even without explicit supervision. |
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| Challenge: | Recurrent neural networks (RNNs) can induce non-trivial properties of language. |
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RNNs can generate bounded hierarchical languages with optimal memory (2020.emnlp-main)
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| Challenge: | Existing studies have shown that RNNs can efficiently generate bounded hierarchical languages with high syntactic fidelity, but their success is not well-understood theoretically. |
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| Challenge: | Recent work by Hewitt et al. (2020) provides an interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs). |
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Recurrent Neural Networks with Mixed Hierarchical Structures and EM Algorithm for Natural Language Processing (2022.lrec-1)
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The Importance of Being Recurrent for Modeling Hierarchical Structure (D18-1)
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| Challenge: | Recent work shows that recurrent neural networks can implicitly capture hierarchical information when trained to solve common natural language processing tasks. |
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| Challenge: | recurrent models have been effective in NLP tasks but performance on context-free languages (CFLs) is weak. |
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Improving Text Understanding via Deep Syntax-Semantics Communication (2020.findings-emnlp)
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| Challenge: | Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance. |
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How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text (2020.coling-main)
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| Challenge: | Recent work in NLP shows that LSTMs capture compositional structure in language data. |
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