# A Joint Model for Question Answering and Question Generation

Source: [A Joint Model for Question Answering and Question Generation](<https://www.sota2.com/research/paper/68fa63631e209131ce9fe4f2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:33.670Z

Authors: Tong Wang, Xingdi Yuan, Adam Trischler

Published: 2017-06-05T17:58:52.000Z

## Abstract

We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents. The proposed model uses a sequence-to-sequence framework that encodes the document and generates a question (answer) given an answer (question). Significant improvement in model performance is observed empirically on the SQuAD corpus, confirming our hypothesis that the model benefits from jointly learning to perform both tasks. We believe the joint model's novelty offers a new perspective on machine comprehension beyond architectural engineering, and serves as a first step towards autonomous information seeking.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1706.01450>)

[PDF](<https://arxiv.org/pdf/1706.01450.pdf>)