| 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. |
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| Challenge: | Large language models are difficult to train because of the growing computation time and cost. |
| Approach: | They propose a highly-efficient architecture that combines fast recurrence and attention for sequence modeling. |
| Outcome: | The proposed model achieves state-of-the-art on a Wiki-103 and Billion Word datasets using 1.6 days of training on an 8-GPU machine. |
Pyramidal Recurrent Unit for Language Modeling (D18-1)
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| Challenge: | Long short term memory units are powerful tools for language modeling, but their performance can be limited by the number of parameters. |
| Approach: | They propose a pyramidal recurrent unit which enables learning representations in high dimensional space with more generalization power and fewer parameters. |
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Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU (2021.emnlp-main)
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| Challenge: | Recent work attempts to apply incremental processing to NLUs but this is computationally expensive and does not scale efficiently for long sequences. |
| Approach: | They propose to apply Transformers incrementally via restart-incrementality by repeatedly feeding, to an unchanged model, increasingly longer input prefixes to produce partial outputs. |
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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. |
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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. |
Efficient Sequence Learning with Group Recurrent Networks (N18-1)
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| 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. |
Modeling Recurrence for Transformer (N19-1)
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| Challenge: | Existing studies show that the lack of recurrence modeling hinders the development of a translation model. |
| Approach: | They propose to model recurrence for Transformer with an additional recurrent encoder. |
| Outcome: | The proposed model outperforms the deep model on EnglishGerman and ChineseEnglish translation tasks. |
Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model (2023.findings-emnlp)
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| Challenge: | Transformers have shown dominant performance across a range of domains including language and vision, but their computational cost grows quadratically with the sequence length, making their usage prohibitive for resource-constrained applications. |
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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. |
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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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