8 citations · 23 across the 13 of their papers we have counts for
13 papers
Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer
Boan Liu, Liang Ding, Li Shen +4
The Mixture of Experts (MoE) has emerged as a highly successful technique in deep learning, based on the principle of divide-and-conquer to maximize model capacity without signific…
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training
Yibin Lei, Liang Ding, Yu Cao +3
Dense retrievers have achieved impressive performance, but their demand for abundant training data limits their application scenarios. Contrastive pre-training, which constructs ps…
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…
Knowledge-Aware Federated Active Learning with Non-IID Data
Yu-Tong Cao, Ye Shi, Baosheng Yu +2
Federated learning enables multiple decentralized clients to learn collaboratively without sharing the local training data. However, the expensive annotation cost to acquire data l…
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…
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…
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…
A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation
Yu Cao, Wei Bi, Meng Fang +2
Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models. However, with limited persona-based dialogue…
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…
DAGN: Discourse-Aware Graph Network for Logical Reasoning
Yinya Huang, Meng Fang, Yu Cao +2
Recent QA with logical reasoning questions requires passage-level relations among the sentences. However, current approaches still focus on sentence-level relations interacting amo…
Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation
Yu Cao, Liang Ding, Zhiliang Tian +1
Dialogue generation models face the challenge of producing generic and repetitive responses. Unlike previous augmentation methods that mostly focus on token manipulation and ignore…
Pretrained Language Models for Dialogue Generation with Multiple Input Sources
Yu Cao, Wei Bi, Meng Fang +1
Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. However, it is still under investigating how to apply them to…
Unsupervised Domain Adaptation on Reading Comprehension
Yu Cao, Meng Fang, Baosheng Yu +1
Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these m…