most citedEfficient Attention via Control Variates

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2023

Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation

Chenxu Yang, Zheng Lin, Lanrui Wang +6

Knowledge-grounded dialogue generation aims to mitigate the issue of text degeneration by incorporating external knowledge to supplement the context. However, the model often fails…

cs.CL2023

Attentive Multi-Layer Perceptron for Non-autoregressive Generation

Shuyang Jiang, Jun Zhang, Jiangtao Feng +2

Autoregressive~(AR) generation almost dominates sequence generation for its efficacy. Recently, non-autoregressive~(NAR) generation gains increasing popularity for its efficiency a…

cs.CL20231 cited

Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking

Qingyue Wang, Liang Ding, Yanan Cao +5

Zero-shot transfer learning for Dialogue State Tracking (DST) helps to handle a variety of task-oriented dialogue domains without the cost of collecting in-domain data. Existing wo…

cs.CV2023

Combo of Thinking and Observing for Outside-Knowledge VQA

Qingyi Si, Yuchen Mo, Zheng Lin +2

Outside-knowledge visual question answering is a challenging task that requires both the acquisition and the use of open-ended real-world knowledge. Some existing solutions draw ex…

cs.LG20231 cited

K-means Clustering Based Feature Consistency Alignment for Label-free Model Evaluation

Shuyu Miao, Lin Zheng, Jingjing Liu +1

The label-free model evaluation aims to predict the model performance on various test sets without relying on ground truths. The main challenge of this task is the absence of label…

cs.CV2023

Co-Salient Object Detection with Co-Representation Purification

Ziyue Zhu, Zhao Zhang, Zheng Lin +2

Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. Mining a co-representation is essential for locating co-salient objects.…