14 citations · 27 across the 11 of their papers we have counts for
11 papers
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…
Responsible Active Learning via Human-in-the-loop Peer Study
Yu-Tong Cao, Jingya Wang, Baosheng Yu +1
Active learning has been proposed to reduce data annotation efforts by only manually labelling representative data samples for training. Meanwhile, recent active learning applicati…
Cross-Modal Contrastive Learning for Robust Reasoning in VQA
Qi Zheng, Chaoyue Wang, Daqing Liu +2
Multi-modal reasoning in visual question answering (VQA) has witnessed rapid progress recently. However, most reasoning models heavily rely on shortcuts learned from training data,…
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…
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
Yu Cao, Dianqi Li, Meng Fang +4
We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while main…