| Challenge: | Existing methods for question answering and question generation are hard to obtain in many domains. |
| Approach: | They propose a method for jointly learning to ask and answer questions . they leverage unlabeled text along with labeled question answer pairs for learning . |
| Outcome: | The proposed method improves on four benchmark datasets on question answering and question generation tasks. |
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| Challenge: | Pretrained language models provide high-quality contextualized word embeddings, but training question answering models requires large amounts of annotated data for specific domains. |
| Approach: | They propose a framework for automatically generating more non-trivial question-answer pairs to improve model performance. |
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Training Question Answering Models From Synthetic Data (2020.emnlp-main)
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| Challenge: | Existing work on question and answer generation aims to improve question answering models given limited amount of labeled data. |
| Approach: | They synthesize questions and answers from a synthetic text corpus generated by an 8.3 billion parameter GPT-2 model and achieve 88.4 Exact Match (EM) and 93.9 F1 score on the SQuAD1.1 dev set. |
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Harvesting and Refining Question-Answer Pairs for Unsupervised QA (2020.acl-main)
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| Challenge: | Recent research attempts to extend unsupervised question answering to settings with few or no labeled data available. |
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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. |
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Semi-supervised Training Data Generation for Multilingual Question Answering (L18-1)
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Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data (2021.emnlp-main)
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| Challenge: | Existing approaches to generate high quality question-answer pairs are limited . a new framework is proposed for the question-answer generation task on real-world examination data. |
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Cross-Lingual Training for Automatic Question Generation (P19-1)
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| Challenge: | Automatic question generation is a challenging problem in natural language understanding . manual curating a dataset of comparable size for a new language is tedious and expensive. |
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Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings (D18-1)
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MixQG: Neural Question Generation with Mixed Answer Types (2022.findings-naacl)
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| Challenge: | Existing neural question generation approaches focus on short factoid type of answers. |
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Answering questions by learning to rank - Learning to rank by answering questions (D19-1)
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| Challenge: | Existing approaches to answer multiple-choice questions with no supporting documents are poor performance. |
| Approach: | They propose a method which can be used to semantically rank documents extracted from Wikipedia . they propose 'semantic ranking' method that latently learns to rank documents by their importance . |
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