178 citations · 736 across the 21 of their papers we have counts for
12 papers · 2 filters
A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification
Pengcheng Yang, Shuming Ma, Yi Zhang +3
Multi-label text classification (MLTC) aims to assign multiple labels to each sample in the dataset. The labels usually have internal correlations. However, traditional methods ten…
Future-Prediction-Based Model for Neural Machine Translation
Bingzhen Wei, Junyang Lin
We propose a novel model for Neural Machine Translation (NMT). Different from the conventional method, our model can predict the future text length and words at each decoding time…
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
Liangchen Luo, Jingjing Xu, Junyang Lin +2
Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and resp…
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation
Junyang Lin, Xu Sun, Xuancheng Ren +2
Most of the Neural Machine Translation (NMT) models are based on the sequence-to-sequence (Seq2Seq) model with an encoder-decoder framework equipped with the attention mechanism. H…
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
Junyang Lin, Qi Su, Pengcheng Yang +2
We propose a novel model for multi-label text classification, which is based on sequence-to-sequence learning. The model generates higher-level semantic unit representations with m…
Deconvolution-Based Global Decoding for Neural Machine Translation
Junyang Lin, Xu Sun, Xuancheng Ren +3
A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following…