DeepAI AI Chat
Log In Sign Up

Improving Neural Question Generation using World Knowledge

09/09/2019
by   Deepak Gupta, et al.
Director, IIT Patna
0

In this paper, we propose a method for incorporating world knowledge (linked entities and fine-grained entity types) into a neural question generation model. This world knowledge helps to encode additional information related to the entities present in the passage required to generate human-like questions. We evaluate our models on both SQuAD and MS MARCO to demonstrate the usefulness of the world knowledge features. The proposed world knowledge enriched question generation model is able to outperform the vanilla neural question generation model by 1.37 and 1.59 absolute BLEU 4 score on SQuAD and MS MARCO test dataset respectively.

READ FULL TEXT

page 1

page 2

page 3

page 4

03/15/2022

K-VQG: Knowledge-aware Visual Question Generation for Common-sense Acquisition

Visual Question Generation (VQG) is a task to generate questions from im...
09/17/2019

Learning to Generate Questions with Adaptive Copying Neural Networks

Automatic question generation is an important problem in natural languag...
12/20/2016

Automatic Generation of Grounded Visual Questions

In this paper, we propose the first model to be able to generate visuall...
11/12/2019

All It Takes is 20 Questions!: A Knowledge Graph Based Approach

20 Questions (20Q) is a two-player game. One player is the answerer, and...
04/28/2022

Instilling Type Knowledge in Language Models via Multi-Task QA

Understanding human language often necessitates understanding entities a...
02/07/2020

Pairing for Generation of Synthetic Populations: the Direct Probabilistic Pairing method

Methods for the Generation of Synthetic Populations do generate the enti...
06/08/2021

Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation

Answering a programming question using only its title is difficult as sa...