activity
20172023
most citedAliMe KG: Domain Knowledge Graph Construction and Application in E-commerce

60 citations · 165 across the 21 of their papers we have counts for

collaborators
Showing 2020Show all

6 papers · 1 filter

cs.CL2020★ 3 cited

Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources

Qianglong Chen, Feng Ji, Haiqing Chen +1

In order to facilitate natural language understanding, the key is to engage commonsense or background knowledge. However, how to engage commonsense effectively in question answerin…

cs.CL2020

Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations

Haojie Pan, Cen Chen, Chengyu Wang +4

Information-seeking conversation systems are increasingly popular in real-world applications, especially for e-commerce companies. To retrieve appropriate responses for users, it i…

cs.AI2020★ 60 cited

AliMe KG: Domain Knowledge Graph Construction and Application in E-commerce

Feng-Lin Li, Hehong Chen, Guohai Xu +4

Pre-sales customer service is of importance to E-commerce platforms as it contributes to optimizing customers' buying process. To better serve users, we propose AliMe KG, a domain…

cs.CL2020★ 1 cited

MTSS: Learn from Multiple Domain Teachers and Become a Multi-domain Dialogue Expert

Shuke Peng, Feng Ji, Zehao Lin +3

How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits t…

cs.CL2020★ 3 cited

Predict-then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems

Zehao Lin, Shaobo Cui, Guodun Li +6

Different people have different habits of describing their intents in conversations. Some people tend to deliberate their intents in several successive utterances, i.e., they use s…

cs.CL2020★ 1 cited

"Wait, I'm Still Talking!" Predicting the Dialogue Interaction Behavior Using Imagine-Then-Arbitrate Model

Zehao Lin, Shaobo Cui, Guodun Li +6

Producing natural and accurate responses like human beings is the ultimate goal of intelligent dialogue agents. So far, most of the past works concentrate on selecting or generatin…