Question Generation

168 papers with code • 9 benchmarks • 22 datasets

The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation.

Source: Generating Highly Relevant Questions

Libraries

Use these libraries to find Question Generation models and implementations

Most implemented papers

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

ashwinkalyan/dbs 7 Oct 2016

We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.

Unified Language Model Pre-training for Natural Language Understanding and Generation

microsoft/unilm NeurIPS 2019

This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.

Learning to Ask: Neural Question Generation for Reading Comprehension

xinyadu/nqg ACL 2017

We study automatic question generation for sentences from text passages in reading comprehension.

Neural Question Generation from Text: A Preliminary Study

magic282/NQG 6 Apr 2017

Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage.

Machine Comprehension by Text-to-Text Neural Question Generation

bloomsburyai/question-generation WS 2017

We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers.

Synthetic QA Corpora Generation with Roundtrip Consistency

patil-suraj/question_generation ACL 2019

We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency.

ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training

microsoft/ProphetNet 13 Jan 2020

This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.

ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation

PaddlePaddle/ERNIE 26 Jan 2020

Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.

Simplifying Paragraph-level Question Generation via Transformer Language Models

patil-suraj/question_generation 3 May 2020

Question generation (QG) is a natural language generation task where a model is trained to ask questions corresponding to some input text.

Learning Dense Representations of Phrases at Scale

jhyuklee/DensePhrases ACL 2021

Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).