| Challenge: | Existing sequence-to-sequence neural models may not be able to identify answer-relevant context words for question generation. |
| Approach: | They propose to model the unstructured sentence and the structured answer-relevant relation for question generation by combining to the point context and unstructure. |
| Outcome: | Experiments show that the proposed model improves on the unstructured sentence and the structured answer-relevant relation. |
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| Challenge: | Recent neural network-based approaches generate interrogative words that do not match the answer type. |
| Approach: | They propose an answer-focused and position-aware neural question generation model to address these issues. |
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Leveraging Context Information for Natural Question Generation (N18-2)
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| Challenge: | Existing work for natural question generation ignores the input passage or hard-codes answer positions. |
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Improving Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)
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| Challenge: | Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers. |
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Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation (2020.aacl-main)
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| Challenge: | Current neural network-based questions generation techniques take only one or two sentences as input. |
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Generating Highly Relevant Questions (D19-1)
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| Challenge: | Existing neural QG models generate generic questions that are not relevant to passages and answers. |
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Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation (2020.acl-main)
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| Challenge: | Question Generation is a simple syntactic transformation but many aspects of semantics influence what questions are good to form. |
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| Challenge: | Existing QG models suffer from a “semantic drift” problem, i.e., the semantics of the model-generated question drifts away from the given context and answer. |
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Context Generation Improves Open Domain Question Answering (2023.findings-eacl)
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Dan Su, Mostofa Patwary, Shrimai Prabhumoye, Peng Xu, Ryan Prenger, Mohammad Shoeybi, Pascale Fung, Anima Anandkumar, Bryan Catanzaro
| Challenge: | Existing closed-book question answering methods do not fully exploit the parameterized knowledge. |
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SGCM: Salience-Guided Context Modeling for Question Generation (2024.lrec-main)
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| Challenge: | Identifying relevant sentences to answers is crucial for reasoning the possible questions before generation. |
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