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Found 4 papers, 2 papers with code
ClusterFormer: Neural Clustering Attention for Efficient and Effective Transformer
Other sparse methods use clustering patterns to select words, but the clustering process is separate from the training process of the target task, which causes a decrease in effectiveness.
Training Multilingual Pre-trained Language Model with Byte-level Subwords
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling
Transformer has been widely-used in many Natural Language Processing (NLP) tasks and the scaled dot-product attention between tokens is a core module of Transformer.
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
