148 citations · 708 across the 141 of their papers we have counts for
22 papers · 1 filter
Original or Translated? On the Use of Parallel Data for Translation Quality Estimation
Baopu Qiu, Liang Ding, Di Wu +3
Machine Translation Quality Estimation (QE) is the task of evaluating translation output in the absence of human-written references. Due to the scarcity of human-labeled QE data, p…
Toward Human-Like Evaluation for Natural Language Generation with Error Analysis
Qingyu Lu, Liang Ding, Liping Xie +3
The state-of-the-art language model-based automatic metrics, e.g. BARTScore, benefiting from large-scale contextualized pre-training, have been successfully used in a wide range of…
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
Qihuang Zhong, Liang Ding, Yibing Zhan +11
This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language…
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…
SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters
Shwai He, Liang Ding, Daize Dong +2
Adapter Tuning, which freezes the pretrained language models (PLMs) and only fine-tunes a few extra modules, becomes an appealing efficient alternative to the full model fine-tunin…
PAD-Net: An Efficient Framework for Dynamic Networks
Shwai He, Liang Ding, Daize Dong +3
Dynamic networks, e.g., Dynamic Convolution (DY-Conv) and the Mixture of Experts (MoE), have been extensively explored as they can considerably improve the model's representation p…
On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation
Changtong Zan, Liang Ding, Li Shen +3
Pre-Training (PT) of text representations has been successfully applied to low-resource Neural Machine Translation (NMT). However, it usually fails to achieve notable gains (someti…
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models
Qihuang Zhong, Liang Ding, Li Shen +4
Fine-tuning large pretrained language models on a limited training corpus usually suffers from poor generalization. Prior works show that the recently-proposed sharpness-aware mini…
Vega-MT: The JD Explore Academy Translation System for WMT22
Changtong Zan, Keqin Peng, Liang Ding +9
We describe the JD Explore Academy's submission of the WMT 2022 shared general translation task. We participated in all high-resource tracks and one medium-resource track, includin…
PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation
Qihuang Zhong, Liang Ding, Juhua Liu +2
Prompt Transfer (PoT) is a recently-proposed approach to improve prompt-tuning, by initializing the target prompt with the existing prompt trained on similar source tasks. However,…
Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks
Chuang Liu, Xueqi Ma, Yibing Zhan +5
Graph Neural Networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and the number of model parameters, which restricts…
Dynamic Contrastive Distillation for Image-Text Retrieval
Jun Rao, Liang Ding, Shuhan Qi +4
Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major d…
Parameter-Efficient and Student-Friendly Knowledge Distillation
Jun Rao, Xv Meng, Liang Ding +2
Knowledge distillation (KD) has been extensively employed to transfer the knowledge from a large teacher model to the smaller students, where the parameters of the teacher are fixe…
Interpretable Proof Generation via Iterative Backward Reasoning
Hanhao Qu, Yu Cao, Jun Gao +2
We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series o…
E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation
Qihuang Zhong, Liang Ding, Juhua Liu +2
Sequence-to-sequence (seq2seq) learning is a popular fashion for large-scale pretraining language models. However, the prior seq2seq pretraining models generally focus on reconstru…
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…
Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation and Understanding
Changtong Zan, Liang Ding, Li Shen +3
For multilingual sequence-to-sequence pretrained language models (multilingual Seq2Seq PLMs), e.g. mBART, the self-supervised pretraining task is trained on a wide range of monolin…
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis
Bing Wang, Liang Ding, Qihuang Zhong +2
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, which focuses on detecting the sentiment polarity towards the aspect in a sentence. However, it is…
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning
Lin Zhang, Li Shen, Liang Ding +2
Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow converg…
Where Does the Performance Improvement Come From? -- A Reproducibility Concern about Image-Text Retrieval
Jun Rao, Fei Wang, Liang Ding +4
This article aims to provide the information retrieval community with some reflections on recent advances in retrieval learning by analyzing the reproducibility of image-text retri…
Improving Neural Machine Translation by Denoising Training
Liang Ding, Keqin Peng, Dacheng Tao
We present a simple and effective pretraining strategy {D}en{o}ising {T}raining DoT for neural machine translation. Specifically, we update the model parameters with source- and ta…
Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis
Qihuang Zhong, Liang Ding, Juhua Liu +3
Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information…