Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models (D19-56)
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| Challenge: | Ambiguous user queries can result in multiple topics being retrieved from search engines. |
| Approach: | They propose a task of generating a common question from multiple documents by training an RNN-based single encoder-decoder generator from document pairs and then a model that aggregates these word distributions to generate a question. |
| Outcome: | The proposed model significantly outperforms existing models when evaluated using automated metrics and human judgments on the MS-MARCO-QA dataset. |
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Woon Sang Cho, Yizhe Zhang, Sudha Rao, Asli Celikyilmaz, Chenyan Xiong, Jianfeng Gao, Mengdi Wang, Bill Dolan
| Challenge: | Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents, but a naive model trained only using the targeted document set may generate too generic questions that cover a larger scope than delineated by the document set. |
| Approach: | They propose a contrastive learning strategy where given ‘positive’ and ‘negative’ sets of documents, generate a question that is closely related to the ‘positive' set but far away from the ‘negative' set. |
| Outcome: | The proposed model significantly outperforms several strong baselines, as measured by automatic metrics and human evaluation. |
Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss (D19-1)
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| Challenge: | Conventional methods for question generation neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to have a definitive answer. |
| Approach: | They propose a neural encoder-decoder model with multi-level copy mechanisms to generate questions . they also introduce answer-aware loss to make generated questions correspond to more definitive answers. |
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Multi-hop Inference for Question-driven Summarization (2020.emnlp-main)
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| Challenge: | Existing methods for summarizing source document for non-factoid questions are lacking in factoidic QA. |
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Learning to Generate Question by Asking Question: A Primal-Dual Approach with Uncommon Word Generation (2022.emnlp-main)
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| Challenge: | Existing automatic question generation methods focus on encoding passage and answer to generate question. |
| Approach: | They propose an automatic question generation approach which integrates question generation with its dual problem, question answering, into a unified primal-dual framework. |
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Learning to Ask: Multi-Decoder Fine-Tuning for Multi-Hop Visual Question Generation with External Knowledge (2026.findings-eacl)
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| Challenge: | Traditional supervised QG methods rely on tokenlevel alignment with fixed gold labels struggle to capture diverse valid question formulations. |
| Approach: | They propose a model-agnostic framework that integrates multimodal inputs with a multi-decoder architecture to optimize for multiple labels per sample. |
| Outcome: | The proposed framework improves fluency, reasoning depth, and relevance of visual questions. |
A Practical Toolkit for Multilingual Question and Answer Generation (2023.acl-demo)
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| Challenge: | Generating questions and answers from text is a challenging task due to the expected structured output. |
| Approach: | They propose an online service for multilingual QAG along with a python package for model fine-tuning, generation, and evaluation. |
| Outcome: | The proposed model is available in eight languages and can be used online or locally via lmqg. |
Multi-Hop Question Generation via Dual-Perspective Keyword Guidance (2025.findings-acl)
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| Challenge: | Existing work fails to fully utilize the guiding potential of keywords and neglect to differentiate the distinct roles of question-specific and document-specific keywords. |
| Approach: | They propose a dual-perspective keyword-guided framework that integrates question and document keywords into the multi-hop question generation process. |
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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. |
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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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. |
| Approach: | They propose to generate engaging questions from multiple images using a visual question generation dataset and establish a series of baselines. |
| Outcome: | The proposed model builds stories behind the image sequence to allow for creativity and experience sharing and hence draw attention to downstream applications. |