76 citations · 167 across the 18 of their papers we have counts for
18 papers
Revisiting Token Dropping Strategy in Efficient BERT Pretraining
Qihuang Zhong, Liang Ding, Juhua Liu +4
Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at s…
Towards Making the Most of ChatGPT for Machine Translation
Keqin Peng, Liang Ding, Qihuang Zhong +5
ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource lan…
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation
Zhaocong Li, Xuebo Liu, Derek F. Wong +2
Transfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT). Existing transfer learning methods f…
Improving Simultaneous Machine Translation with Monolingual Data
Hexuan Deng, Liang Ding, Xuebo Liu +3
Simultaneous machine translation (SiMT) is usually done via sequence-level knowledge distillation (Seq-KD) from a full-sentence neural machine translation (NMT) model. However, the…
Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation
Qian Yang, Qian Chen, Wen Wang +2
Multi-modal multi-hop question answering involves answering a question by reasoning over multiple input sources from different modalities. Existing methods often retrieve evidences…
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling
Zhijun Wang, Xuebo Liu, Min Zhang
Existing research generally treats Chinese character as a minimum unit for representation. However, such Chinese character representation will suffer two bottlenecks: 1) Learning b…
Revisiting Grammatical Error Correction Evaluation and Beyond
Peiyuan Gong, Xuebo Liu, Heyan Huang +1
Pretraining-based (PT-based) automatic evaluation metrics (e.g., BERTScore and BARTScore) have been widely used in several sentence generation tasks (e.g., machine translation and…
SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset
Saihao Huang, Lijie Wang, Zhenghua Li +6
As the first session-level Chinese dataset, CHASE contains two separate parts, i.e., 2,003 sessions manually constructed from scratch (CHASE-C), and 3,456 sessions translated from…
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network
Min Zhang, Zhihong Pan, Xin Zhou +1
Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribut…
BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representation
Zheng Zhang, Liang Ding, Dazhao Cheng +3
Data augmentations (DA) are the cores to achieving robust sequence-to-sequence learning on various natural language processing (NLP) tasks. However, most of the DA approaches force…
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Bei Li, Quan Du, Tao Zhou +7
Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). This paper explores a deeper relationship between Transformer and numerical ODE…
Image-text Retrieval: A Survey on Recent Research and Development
Min Cao, Shiping Li, Juntao Li +2
In the past few years, cross-modal image-text retrieval (ITR) has experienced increased interest in the research community due to its excellent research value and broad real-world…
Learning Semantic-Aligned Feature Representation for Text-based Person Search
Shiping Li, Min Cao, Min Zhang
Text-based person search aims to retrieve images of a certain pedestrian by a textual description. The key challenge of this task is to eliminate the inter-modality gap and achieve…
GreenPCO: An Unsupervised Lightweight Point Cloud Odometry Method
Pranav Kadam, Min Zhang, Jiahao Gu +2
Visual odometry aims to track the incremental motion of an object using the information captured by visual sensors. In this work, we study the point cloud odometry problem, where o…
GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds
Min Zhang, Pranav Kadam, Shan Liu +1
An efficient solution to semantic segmentation of large-scale indoor scene point clouds is proposed in this work. It is named GSIP (Green Segmentation of Indoor Point clouds) and i…
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method
Pranav Kadam, Min Zhang, Shan Liu +1
Inspired by the recent PointHop classification method, an unsupervised 3D point cloud registration method, called R-PointHop, is proposed in this work. R-PointHop first determines…
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
Min Zhang, Yifan Wang, Pranav Kadam +2
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. It has an extremely low training complexity while…
Interpretable Convolutional Neural Networks via Feedforward Design
C. -C. Jay Kuo, Min Zhang, Siyang Li +2
The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any B…