Papers with SRNN

2 papers
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.
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks (2022.emnlp-main)

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Challenge: Causal chain reasoning models suffer from two main transitive problems: threshold effect and scene drift.
Approach: They propose a framework that uses exogenous variables to represent causal pairs and estimates the threshold and scene contradictions using structural causal recurrent neural networks.
Outcome: The proposed framework outperforms baselines on Chinese and English CCR datasets.

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