Search Results for author: Shuangzhi Wu

Found 35 papers, 13 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.

Decoder Machine Translation +2

Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering

no code implementations27 Apr 2024 Chenhao Cui, Yufan Jiang, Shuangzhi Wu, Zhoujun Li

Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question.

Binary Classification Language Modelling +3

DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models

1 code implementation31 Oct 2023 Xinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, Deyi Xiong

The ability of data memorization and regurgitation in pretrained language models, revealed in previous studies, brings the risk of data leakage.

Memorization Model Editing

SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills

no code implementations28 Jun 2023 Zhangyin Feng, Yong Dai, Fan Zhang, Duyu Tang, Xiaocheng Feng, Shuangzhi Wu, Bing Qin, Yunbo Cao, Shuming Shi

Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge.

Natural Language Understanding

Enhancing Large Language Model with Self-Controlled Memory Framework

1 code implementation26 Apr 2023 Bing Wang, Xinnian Liang, Jian Yang, Hui Huang, Shuangzhi Wu, Peihao Wu, Lu Lu, Zejun Ma, Zhoujun Li

Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information.

Book summarization Document Summarization +5

Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

1 code implementation20 Mar 2023 Xinnian Liang, Zefan Zhou, Hui Huang, Shuangzhi Wu, Tong Xiao, Muyun Yang, Zhoujun Li, Chao Bian

We conduct extensive experiments on various Chinese NLP tasks to evaluate existing PLMs as well as the proposed MigBERT.

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

1 code implementation29 Jan 2023 Xinnian Liang, Shuangzhi Wu, Chenhao Cui, Jiaqi Bai, Chao Bian, Zhoujun Li

The global one aims to identify vital sub-topics in the dialogue and the local one aims to select the most important context in each sub-topic.

FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks

no code implementations16 Dec 2022 Weilong Dong, Xinwei Wu, Junzhuo Li, Shuangzhi Wu, Chao Bian, Deyi Xiong

It broadcasts the global model in the server to each client and produces pseudo data for clients so that knowledge from the global model can be explored to enhance few-shot learning of each client model.

Federated Learning Few-Shot Learning +1

Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding

no code implementations7 Nov 2022 Jiali Zeng, Yongjing Yin, Yufan Jiang, Shuangzhi Wu, Yunbo Cao

Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts.

Clustering Contrastive Learning +5

Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization

no code implementations24 Aug 2022 Chenhao Cui, Xinnian Liang, Shuangzhi Wu, Zhoujun Li

The core of ViL-Sum is a joint multi-modal encoder with two well-designed tasks, image reordering and image selection.


An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks

1 code implementation COLING 2022 Xinnian Liang, Jing Li, Shuangzhi Wu, Jiali Zeng, Yufan Jiang, Mu Li, Zhoujun Li

To tackle this problem, in this paper, we proposed an efficient Coarse-to-Fine Facet-Aware Ranking (C2F-FAR) framework for unsupervised long document summarization, which is based on the semantic block.

Document Summarization

Modeling Multi-Granularity Hierarchical Features for Relation Extraction

1 code implementation NAACL 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 Relation Extraction +1

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 NMT +2

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.

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 Binary Classification +2

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.

Decoder Knowledge Distillation +2

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 NMT +1

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 NMT +1

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