Semantic Graphs for Generating Deep Questions

Liangming Pan, Yuxi Xie, Yansong Feng, Tat-Seng Chua, Min-Yen Kan

Abstract Paper Share

Generation Long Paper

Session 2B: Jul 6 (09:00-10:00 GMT)
Session 3B: Jul 6 (13:00-14:00 GMT)
Abstract: This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information about the input passage. In order to capture the global structure of the document and facilitate reasoning, we propose a novel framework that first constructs a semantic-level graph for the input document and then encodes the semantic graph by introducing an attention-based GGNN (Att-GGNN). Afterward, we fuse the document-level and graph-level representations to perform joint training of content selection and question decoding. On the HotpotQA deep-question centric dataset, our model greatly improves performance over questions requiring reasoning over multiple facts, leading to state-of-the-art performance. The code is publicly available at https://github.com/WING-NUS/SG-Deep-Question-Generation.
You can open the pre-recorded video in a separate window.
NOTE: The SlidesLive video may display a random order of the authors. The correct author list is shown at the top of this webpage.

Similar Papers

SciREX: A Challenge Dataset for Document-Level Information Extraction
Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi, Iz Beltagy,
A representative figure from paper main.670
Words Aren't Enough, Their Order Matters: On the Robustness of Grounding Visual Referring Expressions
Arjun Akula, Spandana Gella, Yaser Al-Onaizan, Song-Chun Zhu, Siva Reddy,
A representative figure from paper main.586
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
Yifan Gao, Chien-Sheng Wu, Shafiq Joty, Caiming Xiong, Richard Socher, Irwin King, Michael Lyu, Steven C.H. Hoi,
A representative figure from paper main.88