| Challenge: | Recent advances in machine reading have inspired researchers to combine Information Retrieval with machine reading to tackle open-domain QA. |
| Approach: | They propose two neural network rankers that assign scores to different passages based on their likelihood of containing the answer to a given question. |
| Outcome: | The proposed models achieve human level performance in open-domain QA compared to reading comprehension-style QA because it is difficult to retrieve the pieces of paragraphs that contain the answer to the question. |
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Retrieving Support to Rank Answers in Open-Domain Question Answering (2025.emnlp-main)
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| Challenge: | a novel question answering architecture retrieves content relevant to the combined pair . previous work on automatic claim verification has shown hallucinations . |
| Approach: | They propose a question-answer architecture that prioritizes supporting evidence . it retrieves paragraphs that directly substantiate the correctness of a with respect to q . |
| Outcome: | The proposed approach can be used by large language models to retrieve explanatory paragraphs that ground their reasoning. |
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 . |
| Outcome: | The proposed model achieves state-of-the-art accuracy on two datasets: ARC Easy and Challenge. |
RankQA: Neural Question Answering with Answer Re-Ranking (P19-1)
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| Challenge: | RankQA extends the conventional two-stage process in neural question answering . RankQ achieves state-of-the-art performance on 3 out of 4 benchmark datasets . |
| Approach: | They propose to extend the conventional two-stage process in neural QA with a third stage that performs an additional answer re-ranking. |
| Outcome: | RankQA outperforms more complex question answering systems by a significant margin on 3 out of 4 benchmark datasets. |
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering (D18-1)
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| Challenge: | Recent work has combined open-domain question answering with machine comprehension models to find answers in a large knowledge source. |
| Approach: | They propose a machine comprehension model that ranks paragraphs of retrieved documents for a higher answer recall with less noise. |
| Outcome: | The proposed model improves on four open-domain QA datasets by 7.8% on average. |
End-to-End Training of Neural Retrievers for Open-Domain Question Answering (2021.acl-long)
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Devendra Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L. Hamilton, Bryan Catanzaro
| Challenge: | Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised methods. |
| Approach: | They propose an approach of unsupervised pre-training with the Inverse Cloze Task and masked salient spans followed by supervised finetuning using question-context pairs. |
| Outcome: | The proposed approach outperforms models like REALM and RAG in retrieval accuracy and answer extraction. |
Neural Ranking with Weak Supervision for Open-Domain Question Answering : A Survey (2023.findings-eacl)
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| Challenge: | Neural ranking models require substantial amounts of relevance annotations, which is costly to scale. |
| Approach: | They propose to train a NR model with weak supervision instead of annotations . they use a structured overview of standard WS signals used for training a model . |
| Outcome: | The proposed approach reduces the cost of annotations by using weak supervision instead of a parametric model. |
Ranking and Sampling in Open-Domain Question Answering (D19-1)
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| Challenge: | Existing approaches focus on positive paragraphs which contain the answer during training, making it disturbed by similar but irrelevant paragraphs during testing. |
| Approach: | They propose a ranking model leveraging the paragraph-question and the paragraph relevance to compute a confidence score for each paragraph. |
| Outcome: | Experiments on three datasets show that the proposed model advances the state of the art. |
Pre-Training Methods for Question Reranking (2024.eacl-short)
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| Challenge: | Existing methods for Question Answering to search for semantically similar questions are not suitable for new questions. |
| Approach: | They propose an unsupervised method for retrieving and ranking questions . they use a question retrieval model and a selection model to rerank questions based on their relevance . |
| Outcome: | The proposed method achieves state-of-the-art performance on QRC and Quora-match datasets . it provides better and cheaper access to answers than the system generated them . |
Reader-Guided Passage Reranking for Open-Domain Question Answering (2021.findings-acl)
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| Challenge: | Current open-domain question answering systems follow a Retriever-Reader architecture . current systems do not use a reranker, which reranked passages based on top predictions of the reader . |
| Approach: | They propose a reader-guIDEd reranking method that reranked passages based on top predictions . they show that RIDER achieves 10 to 20 absolute gains in top-1 retrieval accuracy . |
| Outcome: | The proposed method achieves 10 to 20 gains in top-1 retrieval accuracy and 1 to 4 Exact Match gains without training. |
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 . |
| Outcome: | The tutorial will cover cutting-edge research in open-domain question answering (QA) it will cover two-stage retriever-reader approaches, dense retriever and end-to-end training, and retriever free methods . |