Search Results for author: Shaobo Cui

Found 7 papers, 0 papers with code

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

no code implementations18 Aug 2021 Xuming Lin, Shaobo Cui, Zhongzhou Zhao, Wei Zhou, Ji Zhang, Haiqing Chen

With these two synergic representations, we then regroup these phrases into a fine-grained plan, based on which we generate the final long text.

Story Generation

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

no code implementations17 Aug 2021 Shaobo Cui, Xintong Bao, Xuming Lin, Zhongzhou Zhao, Ji Zhang, Wei Zhou, Haiqing Chen

Each one-to-one mapping is associated with a conditional generation pattern and is modeled with an expert in SPMoE.

Paraphrase Generation

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

no code implementations24 Feb 2021 Shaobo Cui, Xintong Bao, Xinxing Zu, Yangyang Guo, Zhongzhou Zhao, Ji Zhang, Haiqing Chen

This pipeline approach, however, is undesired in mining the most appropriate QA pairs from documents since it ignores the connection between question generation and answer extraction, which may lead to incompatible QA pair generation, i. e., the selected answer span is inappropriate for question generation.

Machine Reading Comprehension Question Answering +1

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

no code implementations27 May 2020 Zehao Lin, Shaobo Cui, Guodun Li, Xiaoming Kang, Feng Ji, FengLin Li, Zhongzhou Zhao, Haiqing Chen, Yin Zhang

More specifically, we take advantage of a decision model to help the dialogue system decide whether to wait or answer.

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

no code implementations21 May 2020 Shuke Peng, Feng Ji, Zehao Lin, Shaobo Cui, Haiqing Chen, Yin Zhang

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 the quality of dialogue policy, and further affects the generated response.

DAL: Dual Adversarial Learning for Dialogue Generation

no code implementations WS 2019 Shaobo Cui, Rongzhong Lian, Di Jiang, Yuanfeng Song, Siqi Bao, Yong Jiang

DAL is the first work to innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses.

Dialogue Generation Response Generation

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