Handling Anomalies of Synthetic Questions in Unsupervised Question Answering (2020.coling-main)
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| Challenge: | Existing approaches to improve unsupervised Question Answering (UQA) are expensive and require additional datasets. |
| Approach: | They propose an unsupervised QA approach that generates QA training data automatically. |
| Outcome: | The proposed method improves unsupervised QA significantly across a number of QA tasks. |
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| Challenge: | Recent research attempts to extend unsupervised question answering to settings with few or no labeled data available. |
| Approach: | They propose two approaches to improve unsupervised question answering . first, they harvest lexically and syntactically divergent Wikipedia questions to automatically construct a corpus of question-answer pairs . second, they take advantage of the QA model to extract more appropriate answers . |
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Unsupervised Adaptation of Question Answering Systems via Generative Self-training (2020.emnlp-main)
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| Challenge: | Supervised self-training methods have transformed applied machine learning . however, adapting to target data has received little attention . |
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Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering (2020.acl-main)
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| Challenge: | Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers. |
| Approach: | They propose a method which uses questions generated heuristically from news summaries as a source of training data for a QG system. |
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Regularization of Distinct Strategies for Unsupervised Question Generation (2020.findings-emnlp)
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| Challenge: | Unsupervised question answering (UQA) is a task of answering questions from a context that contains the answer. |
| Approach: | They propose a method to generate higher-quality questions with a teacher-student architecture and a regularization module to avoid bias toward a particular question generation strategy. |
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What do Models Learn from Question Answering Datasets? (2020.emnlp-main)
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| Challenge: | Existing models have outperformed humans on question answering datasets, but they have yet to outperform humans on the task of question answering itself. |
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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. |
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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. |
| Outcome: | The proposed model achieves higher accuracy than the SQUAD1.1 training set questions using synthetic questions and answers than the training set question. |