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Found 8 papers, 3 papers with code
Imitation Learning for Non-Autoregressive Neural Machine Translation
Non-autoregressive translation models (NAT) have achieved impressive inference speedup.
Future-Prediction-Based Model for Neural Machine Translation
We propose a novel model for Neural Machine Translation (NMT).
Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency
Especially, the proposed approach improves the semantic consistency by 4\% in terms of human evaluation.
Building an Ellipsis-aware Chinese Dependency Treebank for Web Text
Web 2. 0 has brought with it numerous user-produced data revealing one's thoughts, experiences, and knowledge, which are a great source for many tasks, such as information extraction, and knowledge base construction.
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method
Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications.
Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning
In this paper, we propose a deep stacking framework to improve the performance on word segmentation tasks with insufficient data by integrating datasets from diverse domains.
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks.
Minimal Effort Back Propagation for Convolutional Neural Networks
As traditional neural network consumes a significant amount of computing resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet effective technique to alleviate this problem.
