@inproceedings{yang-etal-2017-semi,
title = "Semi-Supervised {QA} with Generative Domain-Adaptive Nets",
author = "Yang, Zhilin and
Hu, Junjie and
Salakhutdinov, Ruslan and
Cohen, William",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1096",
doi = "10.18653/v1/P17-1096",
pages = "1040--1050",
abstract = "We study the problem of semi-supervised question answering{---}utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the \textit{Generative Domain-Adaptive Nets}. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text.",
}
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%0 Conference Proceedings
%T Semi-Supervised QA with Generative Domain-Adaptive Nets
%A Yang, Zhilin
%A Hu, Junjie
%A Salakhutdinov, Ruslan
%A Cohen, William
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F yang-etal-2017-semi
%X We study the problem of semi-supervised question answering—utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the Generative Domain-Adaptive Nets. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text.
%R 10.18653/v1/P17-1096
%U https://aclanthology.org/P17-1096
%U https://doi.org/10.18653/v1/P17-1096
%P 1040-1050
Markdown (Informal)
[Semi-Supervised QA with Generative Domain-Adaptive Nets](https://aclanthology.org/P17-1096) (Yang et al., ACL 2017)
ACL
- Zhilin Yang, Junjie Hu, Ruslan Salakhutdinov, and William Cohen. 2017. Semi-Supervised QA with Generative Domain-Adaptive Nets. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1040–1050, Vancouver, Canada. Association for Computational Linguistics.