| Challenge: | Question answering (QA) and question generation (QG) are closely related tasks. |
| Approach: | They propose a training algorithm that generalizes both Generative Adversarial Network and Generating Domain-Adaptive Nets under the question answering scenario. |
| Outcome: | The proposed training algorithm generalizes both Generative Adversarial Network (GAN) and Generating Domain-Adaptive Nets (GDAN) under the question answering scenario. |
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| Challenge: | Existing work adapts QA scores to select high-quality questions, but these scores do not improve QA performance on the target domain. |
| Approach: | They propose to synthesize QA pairs with a question generator on the target domain . they propose to train a Question Value Estimator that estimates usefulness of synthetic questions . |
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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. |
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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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Reinforced Multi-task Approach for Multi-hop Question Generation (2020.coling-main)
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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. |
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| Challenge: | Question Answering (QA) is a growing area of research . state-of-the-art QA models struggle on out-of domain documents without fine-tuning . |
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Answer-driven Deep Question Generation based on Reinforcement Learning (2020.coling-main)
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| Challenge: | Existing methods for deep question generation focus on enhancing document representations, but little attention is paid to the answer information. |
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Domain-agnostic Question-Answering with Adversarial Training (D19-58)
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| Challenge: | Adapting models to new domain without finetuning is a challenging problem in deep learning. |
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Learning Answer Generation using Supervision from Automatic Question Answering Evaluators (2023.acl-long)
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Event Extraction as Question Generation and Answering (2023.acl-short)
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| Challenge: | Recent work on Event Extraction addresses the error propagation issue found in token-based classification approaches. |
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Leveraging QA Datasets to Improve Generative Data Augmentation (2022.emnlp-main)
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| Challenge: | Recent advances in NLP have substantially improved the capability of pretrained language models to generate high-quality text. |
| Approach: | They propose to reformulate data generation as context generation for a given question-answer (QA) pair and leverage QA datasets for training context generators. |
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