77 citations · 206 across the 21 of their papers we have counts for
9 papers · 1 filter
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…
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…
Global Encoding for Abstractive Summarization
Junyang Lin, Xu Sun, Shuming Ma +1
In neural abstractive summarization, the conventional sequence-to-sequence (seq2seq) model often suffers from repetition and semantic irrelevance. To tackle the problem, we propose…
Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency
Bingzhen Wei, Xuancheng Ren, Xu Sun +3
Abstractive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated…