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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)
- Paper ID
- 5631
- Venue
- SIGMOD
- Year
- 2019
- Pagerank
- 4.3079067e-05
- Overall Rank
- 9,667 | 32.75%
- DOI
-
10.1145/3299869.3300100
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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