43 citations · 92 across the 12 of their papers we have counts for
12 papers
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation
Xiangpeng Wei, Heng Yu, Yue Hu +4
The principal task in supervised neural machine translation (NMT) is to learn to generate target sentences conditioned on the source inputs from a set of parallel sentence pairs, a…
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction
Heng Yu, Zijing Dong, Yamin Arefeen +3
RAKI can perform database-free MRI reconstruction by training models using only auto-calibration signal (ACS) from each specific scan. As it trains a separate model for each indivi…
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation
Xiangpeng Wei, Heng Yu, Yue Hu +3
As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid cou…
On Learning Universal Representations Across Languages
Xiangpeng Wei, Rongxiang Weng, Yue Hu +3
Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks. However, exis…
Multiscale Collaborative Deep Models for Neural Machine Translation
Xiangpeng Wei, Heng Yu, Yue Hu +3
Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train. In this paper, we present a Multi…
AR: Auto-Repair the Synthetic Data for Neural Machine Translation
Shanbo Cheng, Shaohui Kuang, Rongxiang Weng +3
Compared with only using limited authentic parallel data as training corpus, many studies have proved that incorporating synthetic parallel data, which generated by back translatio…
GRET: Global Representation Enhanced Transformer
Rongxiang Weng, Haoran Wei, Shujian Huang +4
Transformer, based on the encoder-decoder framework, has achieved state-of-the-art performance on several natural language generation tasks. The encoder maps the words in the input…
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
Rongxiang Weng, Heng Yu, Shujian Huang +2
Pre-training and fine-tuning have achieved great success in the natural language process field. The standard paradigm of exploiting them includes two steps: first, pre-training a m…
Improving Neural Machine Translation with Pre-trained Representation
Rongxiang Weng, Heng Yu, Shujian Huang +2
Monolingual data has been demonstrated to be helpful in improving the translation quality of neural machine translation (NMT). The current methods stay at the usage of word-level k…
Sequence Generation: From Both Sides to the Middle
Long Zhou, Jiajun Zhang, Chengqing Zong +1
The encoder-decoder framework has achieved promising process for many sequence generation tasks, such as neural machine translation and text summarization. Such a framework usually…
Code-Switching for Enhancing NMT with Pre-Specified Translation
Kai Song, Yue Zhang, Heng Yu +3
Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags fo…
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages
Xin Tang, Shanbo Cheng, Loc Do +5
Measuring the semantic similarity between two sentences (or Semantic Textual Similarity - STS) is fundamental in many NLP applications. Despite the remarkable results in supervised…