@inproceedings{wang2017a,
author = {Wang, Tong and Yuan, Xingdi and Trischler, Adam},
title = {A Joint Model for Question Answering and Question Generation},
booktitle = {Learning to generate natural language workshop, ICML 2017},
year = {2017},
month = {August},
abstract = {We propose a machine comprehension model that learns jointly to generate questions and answers 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 question-answering 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 offers a new perspective on machine comprehension beyond architectural engineering, and serves as a first step towards autonomous information seeking.},
url = {http://approjects.co.za/?big=en-us/research/publication/a-joint-model-for-question-answering-and-question-generation-2/},
edition = {Learning to generate natural language workshop, ICML 2017},
}