| Challenge: | Deployed question answering (QA) models need to know when to abstain from answering questions that diverge from their training data. |
| Approach: | They propose a selective question answering under domain shift in which a QA model is tested on a mixture of in-domain and out-of-domain data and must answer (i.e., not abstain on) as many questions as possible. |
| Outcome: | The proposed method answers 56% of questions while maintaining 80% accuracy. |
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Robust Question Answering Through Sub-part Alignment (2021.naacl-main)
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| Challenge: | Current textual question answering models fail to generalize to out-of-domain settings. |
| Approach: | They propose to decompose question and context into smaller units and align them to find the answer. |
| Outcome: | The proposed model is more robust than the standard BERT QA model on adversarial and out-of-domain datasets. |
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering (2023.acl-long)
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| Challenge: | Recent advances in open-domain question answering have demonstrated impressive accuracy on general-purpose domains like Wikipedia. |
| Approach: | They propose a more realistic end-to-end domain shift evaluation setting covering five diverse domains to assess model adaption. |
| Outcome: | The proposed model improves by 24 points when adapted to unsupervised datasets. |
Denoising Distantly Supervised Open-Domain Question Answering (P18-1)
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| Challenge: | Existing DS-QA models ignore rich information contained in other paragraphs and are noisy . Existing systems rely on pre-identified relevant texts, which do not always exist in real-world QA scenarios. |
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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. |
| Approach: | They evaluate BERT-based question answering models on their generalizability to out-of-domain examples, responses to missing or incorrect data, and ability to handle question variations. |
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Open-Domain Question Answering (2020.acl-tutorials)
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| Challenge: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering (QA) |
| Approach: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering . focus will shift to cutting- edge models proposed for open- domain QA . |
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Synthetic Question Value Estimation for Domain Adaptation of Question Answering (2022.acl-long)
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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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When to Read Documents or QA History: On Unified and Selective Open-domain QA (2023.findings-acl)
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| Challenge: | Existing work aims to answer factoid questions from an open set of domains using knowledge sources. |
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Domain Adaptation for Question Answering via Question Classification (2022.coling-1)
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| Challenge: | Question answering systems often experience performance deterioration upon user-generated questions. |
| Approach: | They propose a question classification framework to help QA domains adapt to different domains. |
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You Only Need One Model for Open-domain Question Answering (2022.emnlp-main)
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| Challenge: | Recent approaches to Open-domain Question Answering use external knowledge bases, but have separate parameters and are weakly-coupled during training. |
| Approach: | They propose to use a single question answering model trained end-to-end to retrieve external knowledge and rerank passages with a separate reranked model. |
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Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training (2022.findings-naacl)
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| Challenge: | Existing question generation models require large-scale and high-quality training data. |
| Approach: | They propose an unsupervised domain adaptation approach to combat the lack of training data and domain shift issue with domain data selection and self-training. |
| Outcome: | The proposed approach outperforms baselines on three large datasets with different domain similarities, using a transformer-based pre-trained QG model. |