activity
20192021
most citedOneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach

11 citations · 22 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

5 papers · 1 filter

cs.CL2021

GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation

Xuming Lin, Shaobo Cui, Zhongzhou Zhao +3

Existing data-driven methods can well handle short text generation. However, when applied to the long-text generation scenarios such as story generation or advertising text generat…

cs.CL2021

SPMoE: Generate Multiple Pattern-Aware Outputs with Sparse Pattern Mixture of Experts

Shaobo Cui, Xintong Bao, Xuming Lin +4

Many generation tasks follow a one-to-many mapping relationship: each input could be associated with multiple outputs. Existing methods like Conditional Variational AutoEncoder(CVA…

cs.CL202111 cited

OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach

Shaobo Cui, Xintong Bao, Xinxing Zu +4

Large-scale question-answer (QA) pairs are critical for advancing research areas like machine reading comprehension and question answering. To construct QA pairs from documents req…

cs.CL20201 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.CL201910 cited

DAL: Dual Adversarial Learning for Dialogue Generation

Shaobo Cui, Rongzhong Lian, Di Jiang +3

In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe respo…