| Challenge: | Existing methods to generate human-like questions rely on paraphrases to generate good questions. |
| Approach: | They propose to integrate paraphrase knowledge into question generation to generate human-like questions by combining paraphrases with a back-translation method. |
| Outcome: | The proposed model achieves obvious performance gain over several strong baselines and human evaluation validates that it can ask questions of high quality by leveraging paraphrase knowledge. |
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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. |
| Approach: | They propose a question-aware reward function to maximize the utilization of supporting facts in the context. |
| Outcome: | The proposed model outperforms single-hop neural question generation models on automatic evaluation metrics and human evaluation metrics for quality and coverage of the generated questions. |
Exploring Question-Specific Rewards for Generating Deep Questions (2020.coling-main)
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| Challenge: | Recent question generation approaches use the sequence-to-sequence framework to optimize the log likelihood of ground-truth questions using teacher forcing. |
| Approach: | They propose to optimize for QG-specific objectives via reinforcement learning to improve question quality. |
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Evaluation of Question Generation Needs More References (2023.findings-acl)
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Shinhyeok Oh, Hyojun Go, Hyeongdon Moon, Yunsung Lee, Myeongho Jeong, Hyun Seung Lee, Seungtaek Choi
| Challenge: | Existing evaluations of QG methods rely on single reference-based similarity metrics . multiple (pseudo) references are more effective for QG evaluation . |
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Improving the Robustness of Question Answering Systems to Question Paraphrasing (P19-1)
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| Challenge: | Despite advancement of question answering systems, generalizability of QA models is a topic of concern. |
| Approach: | They propose to use a neural paraphrasing model to generate multiple paraphrased questions for a given source question and a set of paraphrase suggestions to re-train the models. |
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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. |
| Approach: | They propose a simple yet effective technique for question generation from paragraphs . they augment a sequence-to-sequence QG model with dynamic, paragraph-specific dictionary . |
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Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (D19-1)
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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. |
| Approach: | They propose two semantics-enhanced rewards obtained from downstream question paraphrasing and question answering tasks to regularize the QG model to generate semantically valid questions. |
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q2d: Turning Questions into Dialogs to Teach Models How to Search (2023.emnlp-main)
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| Challenge: | Recent dialog generation models use external search APIs to generate grounded responses. |
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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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ParaQG: A System for Generating Questions and Answers from Paragraphs (D19-3)
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| Challenge: | Automated question generation systems generate questions from sentences and paragraphs . manual generation of questions is labour-intensive as it requires reading, parsing and understanding of long passages of text. |
| Approach: | They propose a web-based system for generating questions from sentences and paragraphs . paraQG provides an interactive interface for a user to select answers with visual insights . |
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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. |
| Approach: | They propose a set of syntactic rules which transform declarative sentences into question-answer pairs. |
| Outcome: | The proposed system generates a larger number of highly grammatical and relevant questions than existing QG systems. |