Search Results for author: Shuangzhi Wu

Found 22 papers, 6 papers with code

Tencent Translation System for the WMT21 News Translation Task

no code implementations WMT (EMNLP) 2021 Longyue Wang, Mu Li, Fangxu Liu, Shuming Shi, Zhaopeng Tu, Xing Wang, Shuangzhi Wu, Jiali Zeng, Wen Zhang

Based on our success in the last WMT, we continuously employed advanced techniques such as large batch training, data selection and data filtering.

Data Augmentation Translation

Recurrent Attention for Neural Machine Translation

1 code implementation EMNLP 2021 Jiali Zeng, Shuangzhi Wu, Yongjing Yin, Yufan Jiang, Mu Li

Across an extensive set of experiments on 10 machine translation tasks, we find that RAN models are competitive and outperform their Transformer counterpart in certain scenarios, with fewer parameters and inference time.

Machine Translation Translation

Modeling Multi-Granularity Hierarchical Features for Relation Extraction

1 code implementation9 Apr 2022 Xinnian Liang, Shuangzhi Wu, Mu Li, Zhoujun Li

In this paper, we propose a novel method to extract multi-granularity features based solely on the original input sentences.

Relation Extraction

Learning Confidence for Transformer-based Neural Machine Translation

1 code implementation ACL 2022 Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li

Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success.

Machine Translation Translation

Task-guided Disentangled Tuning for Pretrained Language Models

1 code implementation Findings (ACL) 2022 Jiali Zeng, Yufan Jiang, Shuangzhi Wu, Yongjing Yin, Mu Li

Pretrained language models (PLMs) trained on large-scale unlabeled corpus are typically fine-tuned on task-specific downstream datasets, which have produced state-of-the-art results on various NLP tasks.

Pretrained Language Models

Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context

1 code implementation EMNLP 2021 Xinnian Liang, Shuangzhi Wu, Mu Li, Zhoujun Li

In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices.

Document Embedding Keyphrase Extraction

Attention Calibration for Transformer in Neural Machine Translation

no code implementations ACL 2021 Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li

Attention mechanisms have achieved substantial improvements in neural machine translation by dynamically selecting relevant inputs for different predictions.

Machine Translation Translation

Robust Machine Reading Comprehension by Learning Soft labels

no code implementations COLING 2020 Zhenyu Zhao, Shuangzhi Wu, Muyun Yang, Kehai Chen, Tiejun Zhao

Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels.

Machine Reading Comprehension

Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning

no code implementations6 Nov 2020 Yufan Jiang, Shuangzhi Wu, Jing Gong, Yahui Cheng, Peng Meng, Weiliang Lin, Zhibo Chen, Mu Li

In addition, by transferring knowledge from other kinds of MRC tasks, our model achieves a new state-of-the-art results in both single and ensemble settings.

AutoML Machine Reading Comprehension +1

Source Dependency-Aware Transformer with Supervised Self-Attention

no code implementations5 Sep 2019 Chengyi Wang, Shuangzhi Wu, Shujie Liu

Recently, Transformer has achieved the state-of-the-art performance on many machine translation tasks.

Machine Translation Translation

Accelerating Transformer Decoding via a Hybrid of Self-attention and Recurrent Neural Network

no code implementations5 Sep 2019 Chengyi Wang, Shuangzhi Wu, Shujie Liu

Due to the highly parallelizable architecture, Transformer is faster to train than RNN-based models and popularly used in machine translation tasks.

Knowledge Distillation Machine Translation +1

Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy

no code implementations1 Nov 2018 Pengcheng Yang, Fuli Luo, Shuangzhi Wu, Jingjing Xu, Dong-dong Zhang, Xu sun

In order to avoid such sophisticated alternate optimization, we propose to learn unsupervised word mapping by directly maximizing the mean discrepancy between the distribution of transferred embedding and target embedding.

Cross-Lingual Word Embeddings Density Estimation +4

Regularizing Neural Machine Translation by Target-bidirectional Agreement

no code implementations13 Aug 2018 Zhirui Zhang, Shuangzhi Wu, Shujie Liu, Mu Li, Ming Zhou, Tong Xu

Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation.

Machine Translation Translation

Sequence-to-Dependency Neural Machine Translation

no code implementations ACL 2017 Shuangzhi Wu, Dong-dong Zhang, Nan Yang, Mu Li, Ming Zhou

Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned.

Machine Translation Translation

Cannot find the paper you are looking for? You can Submit a new open access paper.