| Challenge: | recurrent and convolutional neural networks are useful for encoding natural language utterances. |
| Approach: | They propose a model that combines neural representation learning with weighted finite-state automatas to learn a soft version of traditional surface patterns. |
| Outcome: | The proposed model is comparable or better than a BiLSTM baseline and a CNN baseline on three text classification tasks. |
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Recurrent Neural Networks as Weighted Language Recognizers (N18-1)
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| Challenge: | Recent experiments show that RNNs outperform other methods in assigning high probability to held-out English text. |
| Approach: | They focus on the single-layer, ReLU-activation, rational-weight RNNs with softmax . they show that most problems for such RNN are undecidable . |
| Outcome: | The proposed model outperforms other methods in assigning high probability to held-out English text. |
Recurrent Neural Language Models as Probabilistic Finite-state Automata (2023.emnlp-main)
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| Challenge: | Existing studies have focused on the expressive power of recurrent neural network LMs to recognize unweighted formal languages. |
| Approach: | They propose to model a strict subset of probabilistic finite-state automata with RNNs . they show that an RNN requires left(N ||right) neurons to represent an LM . |
| Outcome: | The proposed language models can represent a strict subset of probabilistic distributions expressed by finite-state models. |
Lower Bounds on the Expressivity of Recurrent Neural Language Models (2024.naacl-long)
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| Challenge: | Recent studies of the representational capacity of neural LMs have focused on their ability to recognize formal languages. |
| Approach: | They propose to connect recurrent neural networks (RNNs) as classifiers to finite-state automatas (FSAs) and a probabilistic FSA to characterize their representational capacity. |
| Outcome: | The proposed models can express arbitrary regular LMs with linearly bounded precision. |
Rational Recurrences (D18-1)
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| Challenge: | Recent studies show that neural models lack strong intuitions . recent studies show connections between convolutional neural networks and weighted finite state automata (WFSAs) |
| Approach: | They show that some recurrent neural networks share a connection to weighted finite state automata (WFSAs) they define rational recurrences as recursive hidden state update functions . they propose to use these functions to write forward calculations of a finite set of WFSA's . |
| Outcome: | The proposed model outperforms two baselines on language modeling and text classification. |
On the Representational Capacity of Recurrent Neural Language Models (2023.emnlp-main)
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| Challenge: | Existing studies have focused on LMs as formal languages, but they do not consider language membership. |
| Approach: | They extend the Turing completeness result to the probabilistic case . they show that a rationally weighted RLM can simulate any deterministic Turing machine . |
| Outcome: | The proposed model can simulate any deterministic Turing machine with rationally weighted transitions . the proposed model is based on recurrent neural networks with a rational weighting over strings . |
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings (P18-1)
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| Challenge: | recurrent neural networks have produced significant advances in part-of-speech tagging accuracy . a common feature of these models is the presence of rich initial word encodings . however, word or sub-word information interacts only through subsequent recursive layers . |
| Approach: | They propose to use recurrent neural networks with sentence-level context for initial character and word-based representations. |
| Outcome: | The proposed model has the highest accuracy of all participating systems in the CoNLL 2017 task. |
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 . |
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. |
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. |
On the Practical Computational Power of Finite Precision RNNs for Language Recognition (P18-2)
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| Challenge: | Recurrent Neural Networks (RNNs) are famously known to be Turing complete, but this relies on infinite precision in the states and unbounded computation time. |
| Approach: | They propose to use LSTM and Elman-RNN with ReLU activation to study RNNs . they show that LS and ReLU-RNns can easily implement counting behavior . |
| Outcome: | The LSTM and the Elman-RNN with ReLU activation are stronger than the RNN with squashing activation and the GRU. |