Search Results for author: Rui Dong

Found 18 papers, 4 papers with code

Linguistics from a topological viewpoint

1 code implementation16 Mar 2024 Rui Dong

Typological databases in linguistics are usually categorical-valued.

Topological Data Analysis

DiLogics: Creating Web Automation Programs With Diverse Logics

no code implementations10 Aug 2023 Kevin Pu, Jim Yang, Angel Yuan, Minyi Ma, Rui Dong, Xinyu Wang, Yan Chen, Tovi Grossman

Knowledge workers frequently encounter repetitive web data entry tasks, like updating records or placing orders.

Contrastive Learning for Conversion Rate Prediction

1 code implementation12 Jul 2023 Wentao Ouyang, Rui Dong, Xiuwu Zhang, Chaofeng Guo, Jinmei Luo, Xiangzheng Liu, Yanlong Du

To tailor the contrastive learning task to the CVR prediction problem, we propose embedding masking (EM), rather than feature masking, to create two views of augmented samples.

Contrastive Learning

Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations

no code implementations17 Dec 2022 Jifan Chen, Yuhao Zhang, Lan Liu, Rui Dong, Xinchi Chen, Patrick Ng, William Yang Wang, Zhiheng Huang

There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022).

Decoder Multi-Task Learning

Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification

no code implementations EACL 2021 Rui Dong, David Smith

Starting from the Table Parsing (TAPAS) model developed for question answering (Herzig et al., 2020), we find that modeling table structure improves a language model pre-trained on unstructured text.

Fact Verification Graph Neural Network +5

Multi-Task Neural Model for Agglutinative Language Translation

no code implementations ACL 2020 Yirong Pan, Xiao Li, Yating Yang, Rui Dong

Neural machine translation (NMT) has achieved impressive performance recently by using large-scale parallel corpora.

Decoder Machine Translation +3

Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G

no code implementations29 Mar 2020 Rui Dong, Changyang She, Wibowo Hardjawana, Yonghui Li, Branka Vucetic

To accommodate diverse Quality-of-Service (QoS) requirements in the 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying network conditions.

Quantization Transfer Learning

Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks

no code implementations22 Feb 2020 Changyang She, Rui Dong, Zhouyou Gu, Zhanwei Hou, Yonghui Li, Wibowo Hardjawana, Chenyang Yang, Lingyang Song, Branka Vucetic

In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods.

Edge-computing Federated Learning +1

Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation

no code implementations2 Jan 2020 Yirong Pan, Xiao Li, Yating Yang, Rui Dong

Experimental results show that our morphologically motivated word segmentation method is better suitable for the NMT model, which achieves significant improvements on Turkish-English and Uyghur-Chinese machine translation tasks on account of reducing data sparseness and language complexity.

Machine Translation NMT +2

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin

no code implementations30 Jun 2019 Rui Dong, Changyang She, Wibowo Hardjawana, Yonghui Li, Branka Vucetic

We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server.

Edge-computing Management

Multi-Input Attention for Unsupervised OCR Correction

no code implementations ACL 2018 Rui Dong, David Smith

We propose a novel approach to OCR post-correction that exploits repeated texts in large corpora both as a source of noisy target outputs for unsupervised training and as a source of evidence when decoding.

Decoder Optical Character Recognition (OCR)

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