@inproceedings{sultan-etal-2020-importance,
title = "On the Importance of Diversity in Question Generation for {QA}",
author = "Sultan, Md Arafat and
Chandel, Shubham and
Fernandez Astudillo, Ram{\'o}n and
Castelli, Vittorio",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.500",
doi = "10.18653/v1/2020.acl-main.500",
pages = "5651--5656",
abstract = "Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA). In this paper we ask: Is textual diversity in QG beneficial for downstream QA? Using top-p nucleus sampling to derive samples from a transformer-based question generator, we show that diversity-promoting QG indeed provides better QA training than likelihood maximization approaches such as beam search. We also show that standard QG evaluation metrics such as BLEU, ROUGE and METEOR are inversely correlated with diversity, and propose a diversity-aware intrinsic measure of overall QG quality that correlates well with extrinsic evaluation on QA.",
}
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<abstract>Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA). In this paper we ask: Is textual diversity in QG beneficial for downstream QA? Using top-p nucleus sampling to derive samples from a transformer-based question generator, we show that diversity-promoting QG indeed provides better QA training than likelihood maximization approaches such as beam search. We also show that standard QG evaluation metrics such as BLEU, ROUGE and METEOR are inversely correlated with diversity, and propose a diversity-aware intrinsic measure of overall QG quality that correlates well with extrinsic evaluation on QA.</abstract>
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%0 Conference Proceedings
%T On the Importance of Diversity in Question Generation for QA
%A Sultan, Md Arafat
%A Chandel, Shubham
%A Fernandez Astudillo, Ramón
%A Castelli, Vittorio
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F sultan-etal-2020-importance
%X Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA). In this paper we ask: Is textual diversity in QG beneficial for downstream QA? Using top-p nucleus sampling to derive samples from a transformer-based question generator, we show that diversity-promoting QG indeed provides better QA training than likelihood maximization approaches such as beam search. We also show that standard QG evaluation metrics such as BLEU, ROUGE and METEOR are inversely correlated with diversity, and propose a diversity-aware intrinsic measure of overall QG quality that correlates well with extrinsic evaluation on QA.
%R 10.18653/v1/2020.acl-main.500
%U https://aclanthology.org/2020.acl-main.500
%U https://doi.org/10.18653/v1/2020.acl-main.500
%P 5651-5656
Markdown (Informal)
[On the Importance of Diversity in Question Generation for QA](https://aclanthology.org/2020.acl-main.500) (Sultan et al., ACL 2020)
ACL
- Md Arafat Sultan, Shubham Chandel, Ramón Fernandez Astudillo, and Vittorio Castelli. 2020. On the Importance of Diversity in Question Generation for QA. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5651–5656, Online. Association for Computational Linguistics.