@inproceedings{fei-etal-2021-iterative,
title = "Iterative {GNN}-based Decoder for Question Generation",
author = "Fei, Zichu and
Zhang, Qi and
Zhou, Yaqian",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.201",
doi = "10.18653/v1/2021.emnlp-main.201",
pages = "2573--2582",
abstract = "Natural question generation (QG) aims to generate questions from a passage, and generated questions are answered from the passage. Most models with state-of-the-art performance model the previously generated text at each decoding step. However, (1) they ignore the rich structure information that is hidden in the previously generated text. (2) they ignore the impact of copied words on the passage. We perceive that information in previously generated words serves as auxiliary information in subsequent generation. To address these problems, we design the Iterative Graph Network-based Decoder (IGND) to model the previous generation using a Graph Neural Network at each decoding step. Moreover, our graph model captures dependency relations in the passage that boost the generation. Experimental results demonstrate that our model outperforms the state-of-the-art models with sentence-level QG tasks on SQuAD and MARCO datasets.",
}
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<abstract>Natural question generation (QG) aims to generate questions from a passage, and generated questions are answered from the passage. Most models with state-of-the-art performance model the previously generated text at each decoding step. However, (1) they ignore the rich structure information that is hidden in the previously generated text. (2) they ignore the impact of copied words on the passage. We perceive that information in previously generated words serves as auxiliary information in subsequent generation. To address these problems, we design the Iterative Graph Network-based Decoder (IGND) to model the previous generation using a Graph Neural Network at each decoding step. Moreover, our graph model captures dependency relations in the passage that boost the generation. Experimental results demonstrate that our model outperforms the state-of-the-art models with sentence-level QG tasks on SQuAD and MARCO datasets.</abstract>
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%0 Conference Proceedings
%T Iterative GNN-based Decoder for Question Generation
%A Fei, Zichu
%A Zhang, Qi
%A Zhou, Yaqian
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F fei-etal-2021-iterative
%X Natural question generation (QG) aims to generate questions from a passage, and generated questions are answered from the passage. Most models with state-of-the-art performance model the previously generated text at each decoding step. However, (1) they ignore the rich structure information that is hidden in the previously generated text. (2) they ignore the impact of copied words on the passage. We perceive that information in previously generated words serves as auxiliary information in subsequent generation. To address these problems, we design the Iterative Graph Network-based Decoder (IGND) to model the previous generation using a Graph Neural Network at each decoding step. Moreover, our graph model captures dependency relations in the passage that boost the generation. Experimental results demonstrate that our model outperforms the state-of-the-art models with sentence-level QG tasks on SQuAD and MARCO datasets.
%R 10.18653/v1/2021.emnlp-main.201
%U https://aclanthology.org/2021.emnlp-main.201
%U https://doi.org/10.18653/v1/2021.emnlp-main.201
%P 2573-2582
Markdown (Informal)
[Iterative GNN-based Decoder for Question Generation](https://aclanthology.org/2021.emnlp-main.201) (Fei et al., EMNLP 2021)
ACL
- Zichu Fei, Qi Zhang, and Yaqian Zhou. 2021. Iterative GNN-based Decoder for Question Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2573–2582, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.