607 citations · 1.4k across the 17 of their papers we have counts for
17 papers
Multi-Task Learning in Natural Language Processing: An Overview
Shijie Chen, Yu Zhang, Qiang Yang
Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP). However, directly training deep neural models often suffer from overfitting…
Fisher Deep Domain Adaptation
Yinghua Zhang, Yu Zhang, Ying Wei +3
Deep domain adaptation models learn a neural network in an unlabeled target domain by leveraging the knowledge from a labeled source domain. This can be achieved by learning a doma…
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning
Zheng Li, Xin Li, Ying Wei +3
Joint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem. However, such formulation hinders the effectiveness of supervised methods d…
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Guangneng Hu, Yu Zhang, Qiang Yang
Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data spa…
Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification
Zheng Li, Ying Wei, Yu Zhang +3
Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or…
Learning to Multitask
Yu Zhang, Ying Wei, Qiang Yang
Multitask learning has shown promising performance in many applications and many multitask models have been proposed. In order to identify an effective multitask model for a given…
Parameter Transfer Unit for Deep Neural Networks
Yinghua Zhang, Yu Zhang, Qiang Yang
Parameters in deep neural networks which are trained on large-scale databases can generalize across multiple domains, which is referred as "transferability". Unfortunately, the tra…
Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment
Kaixiang Mo, Yu Zhang, Qiang Yang +1
Dialogue policy transfer enables us to build dialogue policies in a target domain with little data by leveraging knowledge from a source domain with plenty of data. Dialogue senten…
LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text
Guangneng Hu, Yu Zhang, Qiang Yang
Collaborative filtering (CF) is the key technique for recommender systems. Pure CF approaches exploit the user-item interaction data (e.g., clicks, likes, and views) only and suffe…
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
Guangneng Hu, Yu Zhang, Qiang Yang
The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains. Transfe…
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems
Kaixiang Mo, Yu Zhang, Qiang Yang +1
Training a personalized dialogue system requires a lot of data, and the data collected for a single user is usually insufficient. One common practice for this problem is to share t…
Integrating User and Agent Models: A Deep Task-Oriented Dialogue System
Weiyan Wang, Yuxiang WU, Yu Zhang +3
Task-oriented dialogue systems can efficiently serve a large number of customers and relieve people from tedious works. However, existing task-oriented dialogue systems depend on h…
Flexible End-to-End Dialogue System for Knowledge Grounded Conversation
Wenya Zhu, Kaixiang Mo, Yu Zhang +3
In knowledge grounded conversation, domain knowledge plays an important role in a special domain such as Music. The response of knowledge grounded conversation might contain multip…
Learning to Transfer
Ying Wei, Yu Zhang, Qiang Yang
Transfer learning borrows knowledge from a source domain to facilitate learning in a target domain. Two primary issues to be addressed in transfer learning are what and how to tran…
A Survey on Multi-Task Learning
Yu Zhang, Qiang Yang
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generaliz…
A Convex Formulation for Learning Task Relationships in Multi-Task Learning
Yu Zhang, Dit-Yan Yeung
Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we pro…
Multi-Domain Collaborative Filtering
Yu Zhang, Bin Cao, Dit-Yan Yeung
Collaborative filtering is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar inter…