Learning to Generate Questions with Adaptive Copying Neural Networks
Summary: Adaptive copying seq2seq QG with biLSTM encoder, global attention, and a copying decoder for questions from sentences/paragraphs. Outperforms SOTA on BLEU/ROUGE, enabling scalable QA data generation and QA-driven data-management benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xinyuan Lu (Carleton University)
BibTeX Citation
@inproceedings{lu_sigmod19,
title = {{Learning to Generate Questions with Adaptive Copying Neural Networks}},
author = {Lu, Xinyuan},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3300100},
url = {https://dl.acm.org/doi/10.1145/3299869.3300100},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,328 | Deep Learning: Systems and Responsibility | 2021 | SIGMOD | 5.4535681e-05 |
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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