7 citations · 17 across the 8 of their papers we have counts for
8 papers
Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems
Zhendong Chu, Nan Wang, Hongning Wang
Conversational Recommender Systems (CRS) actively elicit user preferences to generate adaptive recommendations. Mainstream reinforcement learning-based CRS solutions heavily rely o…
Meta-Reinforcement Learning via Exploratory Task Clustering
Zhendong Chu, Hongning Wang
Meta-reinforcement learning (meta-RL) aims to quickly solve new tasks by leveraging knowledge from prior tasks. However, previous studies often assume a single mode homogeneous tas…
MiddleGAN: Generate Domain Agnostic Samples for Unsupervised Domain Adaptation
Ye Gao, Zhendong Chu, Hongning Wang +1
In recent years, machine learning has achieved impressive results across different application areas. However, machine learning algorithms do not necessarily perform well on a new…
Meta Policy Learning for Cold-Start Conversational Recommendation
Zhendong Chu, Hongning Wang, Yun Xiao +2
Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained…
Improve Learning from Crowds via Generative Augmentation
Zhendong Chu, Hongning Wang
Crowdsourcing provides an efficient label collection schema for supervised machine learning. However, to control annotation cost, each instance in the crowdsourced data is typicall…
InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources for Distantly-supervised Relation Extraction
Zhendong Chu, Haiyun Jiang, Yanghua Xiao +1
Distant supervision makes it possible to automatically label bags of sentences for relation extraction by leveraging knowledge bases, but suffers from the sparse and noisy bag issu…
Learning from Crowds by Modeling Common Confusions
Zhendong Chu, Jing Ma, Hongning Wang
Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low cost. However, the annotation quality of annotators varies considerably, which imposes new c…
CN-Probase: A Data-driven Approach for Large-scale Chinese Taxonomy Construction
Jindong Chen, Ao Wang, Jiangjie Chen +5
Taxonomies play an important role in machine intelligence. However, most well-known taxonomies are in English, and non-English taxonomies, especially Chinese ones, are still very r…