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

60 citations · 100 across the 12 of their papers we have counts for

collaborators
Showing 2019Show all

6 papers · 1 filter

cs.CL2019★ 1 cited

Transformation of Dense and Sparse Text Representations

Wenpeng Hu, Mengyu Wang, Bing Liu +5

Sparsity is regarded as a desirable property of representations, especially in terms of explanation. However, its usage has been limited due to the gap with dense representations.…

cs.CL2019

Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce

Zhenxin Fu, Feng Ji, Wenpeng Hu +4

Information-seeking conversation system aims at satisfying the information needs of users through conversations. Text matching between a user query and a pre-collected question is…

cs.CL2019★ 3 cited

Task-Oriented Conversation Generation Using Heterogeneous Memory Networks

Zehao Lin, Xinjing Huang, Feng Ji +2

How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks…

cs.CL2019

Teacher-Student Framework Enhanced Multi-domain Dialogue Generation

Shuke Peng, Xinjing Huang, Zehao Lin +3

Dialogue systems dealing with multi-domain tasks are highly required. How to record the state remains a key problem in a task-oriented dialogue system. Normally we use human-define…

cs.CL2019

Simple and Effective Text Matching with Richer Alignment Features

Runqi Yang, Jianhai Zhang, Xing Gao +2

In this paper, we present a fast and strong neural approach for general purpose text matching applications. We explore what is sufficient to build a fast and well-performed text ma…

cs.CL2019★ 1 cited

Review-Driven Answer Generation for Product-Related Questions in E-Commerce

Shiqian Chen, Chenliang Li, Feng Ji +2

The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify…