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

Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases

no code implementations • COLING 2020 • Sheng Bi, Xiya Cheng, Yuan-Fang Li, Yongzhen Wang, Guilin Qi

Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i. e. a set of (connected) triples.

Question Generation Question-Generation

Knowledge-aware Method for Confusing Charge Prediction

no code implementations • 7 Oct 2020 • Xiya Cheng, Sheng Bi, Guilin Qi, Yongzhen Wang

In this paper, we propose a knowledge-attentive neural network model, which introduces legal schematic knowledge about charges and exploit the knowledge hierarchical representation as the discriminative features to differentiate confusing charges.