Ask To The Point: Open-Domain Entity-Centric Question Generation

Yuxiang Liu, Jie Huang, Kevin Chen-Chuan Chang
10/21/2023
cs.CLcs.AI

No official code found. Best implementation: liuyuxiang512/ECQG (4 stars, last commit 2023-10-24).

Abstract

We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. The task aims to generate questions from an entity perspective. To solve ECQG, we propose a coherent PLM-based framework GenCONE with two novel modules: content focusing and question verification. The content focusing module first identifies a focus as "what to ask" to form draft questions, and the question verification module refines the questions afterwards by verifying the answerability. We also construct a large-scale open-domain dataset from SQuAD to support this task. Our extensive experiments demonstrate that GenCONE significantly and consistently outperforms various baselines, and two modules are effective and complementary in generating high-quality questions.

View on arXivView PDF

Code Implementations(1)

liuyuxiang512/ECQGCommunity100%
40PythonOct 19, 20232 years ago

Similar Papers

Cite this paper

@article{liu2023point,
  title  = {Ask To The Point: Open-Domain Entity-Centric Question Generation},
  author = {Yuxiang Liu and Jie Huang and Kevin Chen-Chuan Chang},
  year   = {2023},
  eprint = {2310.14126},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.14126v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2310.14126.svg)](https://codeofpaper.com/paper/2310.14126)

Discussion