no code implementations • WMT (EMNLP) 2020 • Shuangzhi Wu, Xing Wang, Longyue Wang, Fangxu Liu, Jun Xie, Zhaopeng Tu, Shuming Shi, Mu Li
This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks.
1 code implementation • WMT (EMNLP) 2020 • Xing Wang, Zhaopeng Tu, Longyue Wang, Shuming Shi
This paper describes the Tencent AI Lab submission of the WMT2020 shared task on biomedical translation in four language directions: German<->English, English<->German, Chinese<->English and English<->Chinese.
no code implementations • WMT (EMNLP) 2020 • Longyue Wang, Zhaopeng Tu, Xing Wang, Li Ding, Liang Ding, Shuming Shi
This paper describes the Tencent AI Lab’s submission of the WMT 2020 shared task on chat translation in English-German.
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.
no code implementations • WMT (EMNLP) 2021 • Xing Wang, Zhaopeng Tu, Shuming Shi
This paper describes the Tencent AI Lab submission of the WMT2021 shared task on biomedical translation in eight language directions: English-German, English-French, English-Spanish and English-Russian.
1 code implementation • 18 Mar 2024 • Yuan Shi, Bin Xia, Xiaoyu Jin, Xing Wang, Tianyu Zhao, Xin Xia, Xuefeng Xiao, Wenming Yang
To address these challenges, we propose VmambaIR, which introduces State Space Models (SSMs) with linear complexity into comprehensive image restoration tasks.
no code implementations • 18 Mar 2024 • Jen-tse Huang, Eric John Li, Man Ho Lam, Tian Liang, Wenxuan Wang, Youliang Yuan, Wenxiang Jiao, Xing Wang, Zhaopeng Tu, Michael R. Lyu
Additionally, we conduct evaluations across various LLMs and find that GPT-4 outperforms other models on GAMA-Bench, achieving a score of 72. 5.
no code implementations • 16 Mar 2024 • Namyong Park, Xing Wang, Antoine Simoulin, Shuai Yang, Grey Yang, Ryan Rossi, Puja Trivedi, Nesreen Ahmed
To address these limitations, the forward-forward algorithm (FF) was recently proposed as an alternative to BP in the image classification domain, which trains NNs by performing two forward passes over positive and negative data.
no code implementations • 5 Mar 2024 • Ce Chi, Xing Wang, Kexin Yang, Zhiyan Song, Di Jin, Lin Zhu, Chao Deng, Junlan Feng
A channel identifier, a global mixing module and a self-contextual attention module are devised in InjectTST.
1 code implementation • 25 Feb 2024 • Huan Ni, Yubin Zhao, Haiyan Guan, Cheng Jiang, Yongshi Jie, Xing Wang, Yiyang Shen
In this paper, we propose a Transformerbased weakly supervised method for cross-resolution land cover classification using outdated data.
no code implementations • 21 Feb 2024 • Zhiwei He, Binglin Zhou, Hongkun Hao, Aiwei Liu, Xing Wang, Zhaopeng Tu, Zhuosheng Zhang, Rui Wang
Furthermore, we analyze two key factors that contribute to the cross-lingual consistency in text watermarking and propose a defense method that increases the AUC from 0. 67 to 0. 88 under CWRA.
no code implementations • 12 Feb 2024 • Zhengsheng Guo, Zhiwei He, Wenxiang Jiao, Xing Wang, Rui Wang, Kehai Chen, Zhaopeng Tu, Yong Xu, Min Zhang
Motivated by the success of unsupervised neural machine translation (UNMT), we introduce an unsupervised sign language translation and generation network (USLNet), which learns from abundant single-modality (text and video) data without parallel sign language data.
1 code implementation • 23 Jan 2024 • Zhiwei He, Xing Wang, Wenxiang Jiao, Zhuosheng Zhang, Rui Wang, Shuming Shi, Zhaopeng Tu
In this work, we investigate the potential of employing the QE model as the reward model (the QE-based reward model) to predict human preferences for feedback training.
1 code implementation • 28 Dec 2023 • Geyan Ye, Xibao Cai, Houtim Lai, Xing Wang, Junhong Huang, Longyue Wang, Wei Liu, Xiangxiang Zeng
Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery.
no code implementations • 21 Nov 2023 • Yue Xie, Xing Wang, Fumiya Iida, David Howard
This work first discusses a significantly large design space (28 design parameters) for a finger-based soft gripper, including the rarely-explored design space of finger arrangement that is converted to various configurations to arrange individual soft fingers.
1 code implementation • 31 Oct 2023 • Tian Liang, Zhiwei He, Jen-tse Huang, Wenxuan Wang, Wenxiang Jiao, Rui Wang, Yujiu Yang, Zhaopeng Tu, Shuming Shi, Xing Wang
Ideally, an advanced agent should possess the ability to accurately describe a given word using an aggressive description while concurrently maximizing confusion in the conservative description, enhancing its participation in the game.
no code implementations • 12 Jun 2023 • Xing Wang, Zhendong Wang, Kexin Yang, Junlan Feng, Zhiyan Song, Chao Deng, Lin Zhu
To capture the intrinsic patterns of time series, we propose a novel deep learning network architecture, named Multi-resolution Periodic Pattern Network (MPPN), for long-term series forecasting.
1 code implementation • IEEE Transactions on Circuits and Systems for Video Technology 2023 • Guanlin Chen, Pengfei Zhu, Bing Cao, Xing Wang, QinGhua Hu
During the tracking process, a cross-drone mapping mechanism is proposed by using the surrounding information of the drone with promising tracking status as reference, assisting drones that lost targets to re-calibrate, which implements real-time cross-drone information interaction.
1 code implementation • 30 May 2023 • Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Zhaopeng Tu, Shuming Shi
To address the DoT problem, we propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution.
1 code implementation • 18 May 2023 • Jinhui Ye, Wenxiang Jiao, Xing Wang, Zhaopeng Tu, Hui Xiong
It has been a challenging task due to the modality gap between sign videos and texts and the data scarcity of labeled data.
Ranked #3 on Sign Language Translation on CSL-Daily
2 code implementations • 6 May 2023 • Zhiwei He, Tian Liang, Wenxiang Jiao, Zhuosheng Zhang, Yujiu Yang, Rui Wang, Zhaopeng Tu, Shuming Shi, Xing Wang
Compared to typical machine translation that focuses solely on source-to-target mapping, LLM-based translation can potentially mimic the human translation process which might take preparatory steps to ensure high-quality translation.
1 code implementation • 5 Apr 2023 • Wenxiang Jiao, Jen-tse Huang, Wenxuan Wang, Zhiwei He, Tian Liang, Xing Wang, Shuming Shi, Zhaopeng Tu
Therefore, we propose ParroT, a framework to enhance and regulate the translation abilities during chat based on open-source LLMs (e. g., LLaMA), human-written translation and feedback data.
1 code implementation • 28 Feb 2023 • Xing Wang, Kexin Yang, Zhendong Wang, Junlan Feng, Lin Zhu, Juan Zhao, Chao Deng
First, we apply adaptive hybrid graph learning to learn the compound spatial correlations among cell towers.
1 code implementation • 20 Jan 2023 • Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, Shuming Shi, Zhaopeng Tu
By evaluating on a number of benchmark test sets, we find that ChatGPT performs competitively with commercial translation products (e. g., Google Translate) on high-resource European languages but lags behind significantly on low-resource or distant languages.
no code implementations • 10 Nov 2022 • Jigang Tong, Fanhang Yang, Sen yang, Enzeng Dong, Shengzhi Du, Xing Wang, Xianlin Yi
The cosh-attention module reduces the space and time complexity of the attention operation.
1 code implementation • 17 Oct 2022 • Zhiwei He, Xing Wang, Zhaopeng Tu, Shuming Shi, Rui Wang
Finally, our unconstrained system achieves BLEU scores of 17. 0 and 30. 4 for English to/from Livonian.
1 code implementation • 13 Oct 2022 • Jinhui Ye, Wenxiang Jiao, Xing Wang, Zhaopeng Tu
In this paper, to overcome the limitation, we propose a Prompt based domain text Generation (PGEN) approach to produce the large-scale in-domain spoken language text data.
1 code implementation • COLING 2022 • Junjie Yu, Xing Wang, Jiangjiang Zhao, Chunjie Yang, Wenliang Chen
The approach first classifies the auto-annotated instances into two groups: confident instances and uncertain instances, according to the probabilities predicted by a teacher model.
no code implementations • 4 Aug 2022 • Hanwen Kang, Xing Wang
In this work, we propose a deep-learning-based segmentation method to perform accurate semantic segmentation on fused data from a LiDAR-Camera visual sensor.
4 code implementations • 12 Jul 2022 • Jiashi Li, Xin Xia, Wei Li, Huixia Li, Xing Wang, Xuefeng Xiao, Rui Wang, Min Zheng, Xin Pan
Then, Next Hybrid Strategy (NHS) is designed to stack NCB and NTB in an efficient hybrid paradigm, which boosts performance in various downstream tasks.
Ranked #277 on Image Classification on ImageNet
no code implementations • 31 May 2022 • Xing Wang, Yuntian He
We design and implement a temporal convolutional network model to predict sepsis onset.
no code implementations • 31 May 2022 • Xing Wang, Yijun Wang
Federated Learning is a rapidly growing area of research and with various benefits and industry applications.
1 code implementation • 25 May 2022 • Hailong Ma, Xin Xia, Xing Wang, Xuefeng Xiao, Jiashi Li, Min Zheng
Recently, Transformer networks have achieved impressive results on a variety of vision tasks.
no code implementations • 19 May 2022 • Xin Xia, Jiashi Li, Jie Wu, Xing Wang, Xuefeng Xiao, Min Zheng, Rui Wang
We revisit the existing excellent Transformers from the perspective of practical application.
no code implementations • 19 Apr 2022 • Zhuoran Li, Xing Wang, Ling Pan, Lin Zhu, Zhendong Wang, Junlan Feng, Chao Deng, Longbo Huang
A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search.
2 code implementations • 29 Mar 2022 • Wei Li, Xing Wang, Xin Xia, Jie Wu, Jiashi Li, Xuefeng Xiao, Min Zheng, Shiping Wen
Vision Transformers have witnessed prevailing success in a series of vision tasks.
1 code implementation • ACL 2022 • Zhiwei He, Xing Wang, Rui Wang, Shuming Shi, Zhaopeng Tu
By carefully designing experiments, we identify two representative characteristics of the data gap in source: (1) style gap (i. e., translated vs. natural text style) that leads to poor generalization capability; (2) content gap that induces the model to produce hallucination content biased towards the target language.
no code implementations • ACL 2022 • Wenxuan Wang, Wenxiang Jiao, Yongchang Hao, Xing Wang, Shuming Shi, Zhaopeng Tu, Michael Lyu
In this paper, we present a substantial step in better understanding the SOTA sequence-to-sequence (Seq2Seq) pretraining for neural machine translation~(NMT).
no code implementations • 8 Dec 2021 • Hanwen Kang, Xing Wang, Chao Chen
It is vital for robots to recognise and localise fruits before the harvesting in natural orchards.
no code implementations • 1 Nov 2021 • Xing Wang, Juan Zhao, Lin Zhu, Xu Zhou, Zhao Li, Junlan Feng, Chao Deng, Yong Zhang
AMF-STGCN extends GCN by (1) jointly modeling the complex spatial-temporal dependencies in mobile networks, (2) applying attention mechanisms to capture various Receptive Fields of heterogeneous base stations, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting.
no code implementations • 27 Oct 2021 • Cheolhei Lee, Xing Wang, Jianguo Wu, Xiaowei Yue
Active learning is a subfield of machine learning that is devised for design and modeling of systems with highly expensive sampling costs.
1 code implementation • ACL 2021 • Wenxiang Jiao, Xing Wang, Zhaopeng Tu, Shuming Shi, Michael R. Lyu, Irwin King
In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data.
no code implementations • 27 May 2021 • Guoping Huang, Lemao Liu, Xing Wang, Longyue Wang, Huayang Li, Zhaopeng Tu, Chengyan Huang, Shuming Shi
Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed.
no code implementations • 24 Feb 2021 • Xing Wang, Alexander Vinel
In this paper, we benchmark several existing graph neural network (GNN) models on different datasets for link predictions.
no code implementations • 1 Jan 2021 • Xing Wang, Lin Zhu, Juan Zhao, Zhou Xu, Zhao Li, Junlan Feng, Chao Deng
Spatial-temporal data forecasting is of great importance for industries such as telecom network operation and transportation management.
no code implementations • 29 Dec 2020 • Shuyuan Lin, Xing Wang, Guobao Xiao, Yan Yan, Hanzi Wang
In this paper, we propose a novel hierarchical representation via message propagation (HRMP) method for robust model fitting, which simultaneously takes advantages of both the consensus analysis and the preference analysis to estimate the parameters of multiple model instances from data corrupted by outliers, for robust model fitting.
no code implementations • 27 Oct 2020 • Tongtong Li, Xing Wang, Ivan Yotov
In this paper, we focus on investigating the influence on hydrodynamic factors of different coupled computational models describing the interaction between an incompressible fluid and two symmetric elastic or poroelastic structures.
Fluid Dynamics Numerical Analysis Numerical Analysis
1 code implementation • NAACL 2021 • Yongchang Hao, Shilin He, Wenxiang Jiao, Zhaopeng Tu, Michael Lyu, Xing Wang
In addition, experimental results demonstrate that our Multi-Task NAT is complementary to knowledge distillation, the standard knowledge transfer method for NAT.
no code implementations • 18 Oct 2020 • Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney
This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data.
1 code implementation • EMNLP 2020 • Wenxiang Jiao, Xing Wang, Shilin He, Irwin King, Michael R. Lyu, Zhaopeng Tu
First, we train an identification model on the original training data, and use it to distinguish inactive examples and active examples by their sentence-level output probabilities.
no code implementations • 29 Sep 2020 • Xing Wang, Alexander Vinel
Existing exploration strategies in reinforcement learning (RL) often either ignore the history or feedback of search, or are complicated to implement.
no code implementations • 29 Sep 2020 • Xing Wang, Alexander Vinel
In this work, we propose a novel cross Q-learning algorithm, aim at alleviating the well-known overestimation problem in value-based reinforcement learning methods, particularly in the deep Q-networks where the overestimation is exaggerated by function approximation errors.
no code implementations • 29 Sep 2020 • Xing Wang, Yijun Wang, Bin Weng, Aleksandr Vinel
We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market.
no code implementations • 15 May 2020 • Xin Xia, Xuefeng Xiao, Xing Wang, Min Zheng
In this way, PAD-NAS can automatically design the operations for each layer and achieve a trade-off between search space quality and model diversity.
1 code implementation • ACL 2020 • Xinwei Geng, Long-Yue Wang, Xing Wang, Bing Qin, Ting Liu, Zhaopeng Tu
Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words.
no code implementations • 28 Apr 2020 • Shilin He, Xing Wang, Shuming Shi, Michael R. Lyu, Zhaopeng Tu
In this paper, we bridge the gap by assessing the bilingual knowledge learned by NMT models with phrase table -- an interpretable table of bilingual lexicons.
no code implementations • 22 Nov 2019 • Jian Li, Xing Wang, Baosong Yang, Shuming Shi, Michael R. Lyu, Zhaopeng Tu
Starting from this intuition, we propose a novel approach to compose representations learned by different components in neural machine translation (e. g., multi-layer networks or multi-head attention), based on modeling strong interactions among neurons in the representation vectors.
no code implementations • IJCNLP 2019 • Jie Hao, Xing Wang, Shuming Shi, Jinfeng Zhang, Zhaopeng Tu
Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information.
no code implementations • IJCNLP 2019 • Jie Hao, Xing Wang, Shuming Shi, Jinfeng Zhang, Zhaopeng Tu
Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work.
no code implementations • IJCNLP 2019 • Long-Yue Wang, Zhaopeng Tu, Xing Wang, Shuming Shi
In this paper, we propose a unified and discourse-aware ZP translation approach for neural MT models.
no code implementations • IJCNLP 2019 • Xing Wang, Zhaopeng Tu, Long-Yue Wang, Shuming Shi
Although self-attention networks (SANs) have advanced the state-of-the-art on various NLP tasks, one criticism of SANs is their ability of encoding positions of input words (Shaw et al., 2018).
no code implementations • IJCNLP 2019 • Shilin He, Zhaopeng Tu, Xing Wang, Long-Yue Wang, Michael R. Lyu, Shuming Shi
Although neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, the interpretability of NMT remains unsatisfactory.
no code implementations • ACL 2019 • Xing Wang, Zhaopeng Tu, Long-Yue Wang, Shuming Shi
In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT).
no code implementations • 10 May 2019 • Xing Wang, Jun Wang, Carlotta Domeniconi, Guoxian Yu, Guo-Qiang Xiao, Maozu Guo
To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering.
no code implementations • NAACL 2019 • Jian Li, Baosong Yang, Zi-Yi Dou, Xing Wang, Michael R. Lyu, Zhaopeng Tu
Multi-head attention is appealing for its ability to jointly extract different types of information from multiple representation subspaces.
no code implementations • NAACL 2019 • Jie Hao, Xing Wang, Baosong Yang, Long-Yue Wang, Jinfeng Zhang, Zhaopeng Tu
In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention and recurrent networks.
no code implementations • 15 Feb 2019 • Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Long-Yue Wang, Shuming Shi, Tong Zhang
With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation.
no code implementations • 15 Feb 2019 • Baosong Yang, Jian Li, Derek Wong, Lidia S. Chao, Xing Wang, Zhaopeng Tu
Self-attention model have shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies.
no code implementations • 26 Dec 2018 • Xinwei Geng, Long-Yue Wang, Xing Wang, Bing Qin, Ting Liu, Zhaopeng Tu
Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process.
no code implementations • EMNLP 2018 • Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Shuming Shi, Tong Zhang
Advanced neural machine translation (NMT) models generally implement encoder and decoder as multiple layers, which allows systems to model complex functions and capture complicated linguistic structures.
no code implementations • 1 Oct 2018 • Xiang Li, Qitian Chen, Xing Wang, Ning Guo, Nan Wu, Quanzheng Li
In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems.
no code implementations • EMNLP 2017 • Xing Wang, Zhaopeng Tu, Deyi Xiong, Min Zhang
Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does.
no code implementations • 17 Oct 2016 • Xing Wang, Zhengdong Lu, Zhaopeng Tu, Hang Li, Deyi Xiong, Min Zhang
Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years.
no code implementations • 26 Aug 2016 • Xing Wang, Jie Liang
Deep neural networks generally involve some layers with mil- lions of parameters, making them difficult to be deployed and updated on devices with limited resources such as mobile phones and other smart embedded systems.