Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model

no code implementations • Findings (EMNLP) 2021 • Sheng Bi, Xiya Cheng, Yuan-Fang Li, Lizhen Qu, Shirong Shen, Guilin Qi, Lu Pan, Yinlin Jiang

The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation.

Question Generation Question-Generation +1

Event Extraction as Multi-turn Question Answering

no code implementations • Findings of the Association for Computational Linguistics 2020 • Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, Yong Zhu

Most traditional approaches formulate this task as classification problems, with event types or argument roles taken as golden labels.

Event Extraction Question Answering +2

Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks

no code implementations • 9 May 2020 • Junheng Huang, Lu Pan, Kang Xu, Weihua Peng, Fayuan Li

In this paper, we propose a novel generation model based on Topic-aware Pointer-Generator Networks (TPGN), which can utilize the topic information hidden in the articles to guide the generation of pertinent and diversified comments.

Comment Generation Text Generation