| Challenge: | Recent work shows that recurrent neural networks can implicitly capture hierarchical information when trained to solve common natural language processing tasks. |
| Approach: | They propose a convolutional sequence-to-sequence model that exploits hierarchical information implicitly. |
| Outcome: | The proposed model is recurrent and non-recurrent, and it can model hierarchical structure implicitly. |
Similar Papers
On Efficiently Representing Regular Languages as RNNs (2024.findings-acl)
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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). |
| Approach: | They generalize their construction and show that RNNs can efficiently represent a larger class of LMs than previously claimed. |
| Outcome: | The results suggest that RNNs can represent a larger class of LMs than previously claimed . |
Recurrent Neural Networks with Mixed Hierarchical Structures and EM Algorithm for Natural Language Processing (2022.lrec-1)
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| Challenge: | A variety of hierarchical RNN models have been proposed to incorporate hierarchically-based hierarchic information in modeling languages in the literature. |
| Approach: | They propose a latent indicator layer approach to identify and learn hierarchical information and develop an EM algorithm to handle the latent indicators layer in training. |
| Outcome: | The proposed approach outperforms other RNN-based models in document classification tasks. |
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. |
Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons (D19-1)
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| Challenge: | Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work. |
| Approach: | They propose to use an advanced variant of self-attention networks (SANs) to enhance the strength of hybrid models by introducing a syntax-oriented inductive bias to perform tree-like composition. |
| Outcome: | The proposed model outperforms both individual models and a standard hybrid model on a machine translation task. |
Implicit n-grams Induced by Recurrence (2022.naacl-main)
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| Challenge: | Recent studies show that self-attention based models have limitations on modeling sequential transformations. |
| Approach: | They propose to extract some explainable features from trained RNNs that are reminiscent of classical n-grams features. |
| Outcome: | The proposed models can model interesting linguistic phenomena such as negation and intensification. |
Colorless Green Recurrent Networks Dream Hierarchically (N18-1)
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| Challenge: | Recurrent neural networks (RNNs) can induce non-trivial properties of language. |
| Approach: | They investigate whether RNNs can track hierarchical syntactic structure . they include nonsensical sentences where RNN cannot rely on semantic cues . |
| Outcome: | The proposed models can predict long-distance agreement in nonsensical sentences in Italian and English. |
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference (D18-1)
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| Challenge: | In this paper, we examine the behavior of deep learning models in their intermediate layers . saliency determines what is critical for the final decision of a deep model . |
| Approach: | They propose to interpret the intermediate layers of deep models by visualizing the saliency of attention and LSTM gating signals. |
| Outcome: | The proposed methods reveal interesting insights and identify critical information contributing to the model decisions. |
Convolutional Neural Networks with Recurrent Neural Filters (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) use recurrent neural networks as convolution filters to capture language compositionality and long-term dependencies. |
| Approach: | They propose to use recurrent neural networks (RNNs) as convolution filters to capture language compositionality and long-term dependencies. |
| Outcome: | The proposed convolutional neural networks achieve state-of-the-art on two sentences and the Stanford Sentiment Treebank. |
A Formal Hierarchy of RNN Architectures (2020.acl-main)
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| Challenge: | Existing theories of expressive power of RNNs are limited. |
| Approach: | They propose a formal hierarchy of the expressive capacity of RNN architectures based on two formal properties: space complexity and rational recurrence. |
| Outcome: | The proposed model is based on the theory of “saturated” RNNs and shows that it obeys a similar hierarchy to unsaturated RNN models. |
Deep RNNs Encode Soft Hierarchical Syntax (P18-2)
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| 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. |