4 citations · 7 across the 5 of their papers we have counts for
6 papers
Dual sparse training framework: inducing activation map sparsity via Transformed regularization
Xiaolong Yu, Cong Tian
Although deep convolutional neural networks have achieved rapid development, it is challenging to widely promote and apply these models on low-power devices, due to computational a…
Controllable Mixed-Initiative Dialogue Generation through Prompting
Maximillian Chen, Xiao Yu, Weiyan Shi +2
Mixed-initiative dialogue tasks involve repeated exchanges of information and conversational control. Conversational agents gain control by generating responses that follow particu…
Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy Planning
Xiao Yu, Maximillian Chen, Zhou Yu
Planning for goal-oriented dialogue often requires simulating future dialogue interactions and estimating task progress. Many approaches thus consider training neural networks to p…
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning
Xiao Yu, Qingyang Wu, Kun Qian +1
In task-oriented dialogs (TOD), reinforcement learning (RL) algorithms train a model to directly optimize response for task-related metrics. However, RL needs to perform exploratio…
Improving Model Training via Self-learned Label Representations
Xiao Yu, Nakul Verma
Modern neural network architectures have shown remarkable success in several large-scale classification and prediction tasks. Part of the success of these architectures is their fl…
FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric
Maximillian Chen, Caitlyn Chen, Xiao Yu +1
Syntax is a fundamental component of language, yet few metrics have been employed to capture syntactic similarity or coherence at the utterance- and document-level. The existing st…