activity
20192021
most citedEmotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction

17 citations · 20 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CL202117 cited

Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction

Wei Wei, Jiayi Liu, Xianling Mao +4

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge i…

cs.CL2021

NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension

Zhixiang Chen, Yikun Lei, Pai Liu +1

SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension. Most existing approaches propose to concat question and o…

cs.IR2020

User-based Network Embedding for Collective Opinion Spammer Detection

Ziyang Wang, Wei Wei, Xian-Ling Mao +3

Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation. To further enhance the infl…

cs.CL20203 cited

Target Guided Emotion Aware Chat Machine

Wei Wei, Jiayi Liu, Xianling Mao +5

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge i…

cs.IR2019

Research Commentary on Recommendations with Side Information: A Survey and Research Directions

Zhu Sun, Qing Guo, Jie Yang +4

Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data spa…

cs.IR2019

Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

Fajie Yuan, Xiangnan He, Haochuan Jiang +4

Session-based recommender systems have attracted much attention recently. To capture the sequential dependencies, existing methods resort either to data augmentation techniques or…