| Challenge: | Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation and speech recognition. |
| Approach: | They propose an efficient architecture to improve the efficiency of such RNN model training by adopting the group strategy for recurrent layers while exploiting the representation rearrangement strategy between layers as well as time steps. |
| Outcome: | The proposed architecture achieves comparable or better accuracy compared with baselines, with a much smaller number of parameters and at a lower computational cost. |
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Sliced Recurrent Neural Networks (C18-1)
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| Challenge: | Recurrent neural networks have difficulty in parallelization because of their recurrent structure. |
| Approach: | They propose sliced recurrent neural networks (SRNNs) which can be parallelized by slicing sequences into many subsequences. |
| Outcome: | The proposed recurrent neural networks perform better than standard RNNs on six large-scale sentiment analysis datasets. |
A Lightweight Recurrent Network for Sequence Modeling (P19-1)
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| Challenge: | Recent studies show that recurrent networks suffer from severe computational inefficiency due to weak parallelization. |
| Approach: | They propose a lightweight recurrent network (LRN) that uses input and forget gates to handle long-range dependencies and gradient vanishing and explosion. |
| Outcome: | The proposed recurrent network yields the best running efficiency on six NLP tasks. |
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. |
| 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. |
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. |
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Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models (N19-1)
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| Challenge: | a technique that uses large corpus n-gram statistics as a regularizer for training a neural network LM on a smaller corpus is effective, and more time-efficient than training on ngrams. |
| Approach: | They propose a technique that uses large corpus n-gram statistics as a regularizer for training on a smaller corpus. |
| Outcome: | The proposed technique is effective and more time-efficient than training on a larger corpus. |
Effective Batching for Recurrent Neural Network Grammars (2021.findings-acl)
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| Challenge: | RNNGs are hard to scale due to the difficulty of batched training. |
| Approach: | They propose to batch RNNGs where every operation is computed in parallel with tensors across multiple sentences. |
| Outcome: | The proposed RNNG scales faster than existing models and achieves x6 speedup compared to existing C++ DyNet implementation . |
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 . |
Advancing Regular Language Reasoning in Linear Recurrent Neural Networks (2024.naacl-short)
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| Challenge: | Existing linear recurrent neural networks have been used for natural language and long-range modeling for decades. |
| Approach: | They propose a linear recurrent neural network with a block-diagonal transition matrix and a transition matrix for LRNNs. |
| Outcome: | The proposed model is the only one capable of performing length extrapolation on regular language tasks such as Sum, Even Pair, and Modular Arithmetic. |
Simple Recurrent Units for Highly Parallelizable Recurrence (D18-1)
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| Challenge: | recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. |
| Approach: | They propose a simple recurrent unit that provides expressive recurrence and allows highly parallel implementation. |
| Outcome: | The proposed model achieves 5—9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets and delivers stronger results than LS and convolutional models. |
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (P18-4)
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| Challenge: | Using RETURNN, we train and decode attention models for translation and speech recognition. |
| Approach: | They propose a layer-wise pretraining scheme for recurrent attention models and show its significant effect on deep recurrence encoder networks. |
| Outcome: | The proposed training and decoding scheme improves 1% on expected training and improves on WMT 2017 and Switchboard. |