Papers with CQG
CoHS-CQG: Context and History Selection for Conversational Question Generation (2022.coling-1)
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| Challenge: | Existing studies focus on single-turn question generation, but few studies have studied the challenges of multiturn QG. |
| Approach: | They propose a two-stage conversational question generation framework that shortens the context and history of the input and calculates relevance scores. |
| Outcome: | The proposed framework achieves state-of-the-art on CoQA in answer-aware and answer-unaware settings. |
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. |
| Outcome: | The proposed method is based on a question-answering style conversation dataset . it can be used to generate meaningful questions on QA and SQuAD datasets . |
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. |
| Approach: | They propose a triple-wise model with hierarchical variations for open-domain conversational question generation using a post-question-answer triple and one-to-many semantic mappings. |
| Outcome: | The proposed model significantly improves the quality of questions in terms of fluency, coherence and diversity over baselines. |
Planning First, Question Second: An LLM-Guided Method for Controllable Question Generation (2024.findings-acl)
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| Challenge: | Current CQG methods focus on difficulty control, neglecting the control of question content and assessed abilities, which are also crucial in educational QG. |
| Approach: | They propose an LLM-guided method PFQS which utilizes Llama 2 to generate an answer plan and then generates questions based on it. |
| Outcome: | The proposed method outperforms state-of-the-art methods and achieves better consistency with requirements in a zero-shot setting. |
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation (2022.acl-long)
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| Challenge: | Current models can not ensure the complexity of generated questions, so they may generate shallow questions that can be answered without multi-hop reasoning. |
| Approach: | They propose a controlled framework to generate multi-hop questions that contain key entities in multi- hop reasoning chains and a novel Transformer-based decoder to guarantee that key entities appear in the questions. |
| Outcome: | The proposed model outperforms the state-of-the-art model 25% on HotpotQA. |
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. |
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation (2023.acl-long)
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| Challenge: | Existing methods to generate conversational question are naive and do not account for the answer span. |
| Approach: | They propose a framework for generating a conversational question from a context. |
| Outcome: | The proposed framework achieves state-of-the-art in two different settings compared to existing models . it uses a sentence as the rationale and extracts the answer span from it . |
PCQPR: Proactive Conversational Question Planning with Reflection (2024.emnlp-main)
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| Challenge: | Current CQG methods focus on immediate context without strategic consideration of the specified conversational outcome. |
| Approach: | They propose a method that uses a planning algorithm inspired by Monte Carlo Tree Search to generate contextually relevant questions. |
| Outcome: | The proposed approach surpasses existing methods in e-learning and customer service fields . it generates contextually appropriate questions strategically devised to reach a specified outcome . |