Consecutive Question Generation via Dynamic Multitask Learning (2022.findings-emnlp)
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| Challenge: | , . ; ) ()((); ()) .())((2): ""(). |
| Approach: | They propose a sequential sequential question-answer generation task with auxiliary tasks generating other elements to generate logically related question-anchor pairs to understand a passage. |
| Outcome: | The proposed framework improves question generation significantly and benefit multiple related tasks. |
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| Challenge: | Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method . large-scale highquality conversational question answering datasets such as CoQA and QuAC can help train models to answer sequential questions. |
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| Challenge: | Empirical evaluation shows our model to outperform the single-hop question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions. |
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Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph Interaction (2020.acl-main)
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Multi-Task Learning with Language Modeling for Question Generation (D19-1)
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| Challenge: | Existing methods for question generation from knowledge bases rely on extensive pre- and post-processing of the input triple. |
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Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases (2022.coling-1)
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| Challenge: | Existing methods train one encoder-decoder-based model to fit all questions . however, such a one-size-fits-all strategy may not perform well for complex questions involving multiple KB relations or functional constraints. |
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ParaQG: A System for Generating Questions and Answers from Paragraphs (D19-3)
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CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation (2022.acl-long)
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Multi-VQG: Generating Engaging Questions for Multiple Images (2022.emnlp-main)
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| Challenge: | Traditional visual question generation (VQG) focuses on single images, resulting in a limited ability to comprehend time-series information of the underlying event. |
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