Reinforcement learning-driven deep question generation with rich semantics

Menghong Guan, Subrota Kumar Mondal, Hong Ning Dai*, Haiyong Bao

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

4 Citations (Scopus)

Abstract

Deep question generation (DQG) refers to generating a complex question from different sentences in context. Existing methods mainly focus on enhancing information extraction based on the encoder–decoder neural networks though they cannot perform well in DQG tasks. To address this issue, we consider combining reinforcement learning with semantic-rich information to generate deep questions in this paper. In particular, we propose a Semantic-Rich Reinforcement Learning Deep Question Generation (SRL-DQG) model, which better utilizes the semantic graphs of document representations based on the Gated Graph Neural Network (GGNN). In order to generate high-quality questions, we also optimize specific objectives via reinforcement learning with consideration of four evaluation factors including naturality, relevance, answerability, and difficulty. Empirical evaluations demonstrate that our SRL-DQG effectively improves the quality of generated questions and achieves superior performance than existing methods in terms of multiple performance metrics. Specifically, we show that several BLEU-n scores were improved by 3.5% to 10% after running SRL-DQG on 6072 samples of HotPotQA.

Original languageEnglish
Article number103232
JournalInformation Processing and Management
Volume60
Issue number2
Early online date17 Dec 2022
DOIs
Publication statusPublished - Mar 2023

Scopus Subject Areas

  • Information Systems
  • Library and Information Sciences
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research

User-Defined Keywords

  • Deep question generation
  • Gated Graph Neural Network
  • Reinforcement learning
  • Semantic graphs

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