Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling (P19-1)
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| Challenge: | Extensive experiments show that our system outperforms several baselines and can generate highly conversational questions. |
| Approach: | They propose a neural model that generates interconnected questions in question-answering style conversations. |
| Outcome: | The proposed model outperforms baselines and can generate highly conversational questions. |
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| Challenge: | Current datasets for conversational question answering lack realistic, domain-specific training data. |
| Approach: | They propose a model that generates question-answer representations across dialogue turns . they use flow propagation training to improve conversational flow and fluidity . |
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Improving Question Generation With to the Point Context (D19-1)
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| 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. |
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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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Controllable Conversation Generation with Conversation Structures via Diffusion Models (2023.findings-acl)
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| Challenge: | Current generation models fail to effectively utilize rich linguistic and world knowledge to generate coherent long text. |
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Reinforced Dynamic Reasoning for Conversational Question Generation (P19-1)
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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. |
| Approach: | They propose a task called Conversational Question Generation which generates a question based on a passage and a conversation history to generate the next question. |
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GTM: A Generative Triple-wise Model for Conversational Question Generation (2021.acl-long)
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| Challenge: | Experimental results show that opendomain conversational question generation improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines. |
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Towards Answer-unaware Conversational Question Generation (D19-58)
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| Challenge: | Existing frameworks for conversational question generation are answeraware, but are not able to generate corresponding answers . a number of question generation methods are developed for text-based question answering . |
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Answer-focused and Position-aware Neural Question Generation (D18-1)
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| Challenge: | Recent neural network-based approaches generate interrogative words that do not match the answer type. |
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Towards automatically generating Questions under Discussion to link information and discourse structure (2020.coling-main)
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| Challenge: | Questions under Discussion (QUD) are emerging as a useful approach to spelling out the connection between information structure of sentences and nature of discourse. |
| Approach: | They propose a framework for QUD annotation based on explicit pragmatic principles . they propose generating all potentially relevant questions for a given sentence . |
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Conversational QA Dataset Generation with Answer Revision (2022.coling-1)
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| Challenge: | Existing frameworks for conversational question-answer generation generate a large-scale dataset based on input passages. |
| Approach: | They propose a conversational question-answer generation framework that extracts question-worthy phrases from passages and generates corresponding questions considering previous conversations. |
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