98 citations · 188 across the 17 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
eess.AS2019★ 5 cited
Synchronous Transformers for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Ye Bai +3
For most of the attention-based sequence-to-sequence models, the decoder predicts the output sequence conditioned on the entire input sequence processed by the encoder. The asynchr…
eess.AS2019
Integrating Knowledge into End-to-End Speech Recognition from External Text-Only Data
Ye Bai, Jiangyan Yi, Jianhua Tao +3
Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because of the end-to-end training, an AED model is usually trained…
cs.CL2019★ 98 cited
A Tensorized Transformer for Language Modeling
Xindian Ma, Peng Zhang, Shuai Zhang +4
Latest development of neural models has connected the encoder and decoder through a self-attention mechanism. In particular, Transformer, which is solely based on self-attention, h…