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. |
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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. |
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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 . |
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Training a Ranking Function for Open-Domain Question Answering (N18-4)
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| 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. |
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Multi-Hop Paragraph Retrieval for Open-Domain Question Answering (P19-1)
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| Challenge: | Existing methods for textual question answering are capable of outperforming humans on certain tasks. |
| Approach: | They propose a method for retrieving multiple supporting paragraphs from a large knowledge base. |
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A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections (2021.acl-long)
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| Challenge: | Question answering systems typically use pipelines that retrieve documents at finer text granularities. |
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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 . |
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Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation (2020.aacl-main)
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| Challenge: | Current neural network-based questions generation techniques take only one or two sentences as input. |
| Approach: | They propose a simple yet effective technique for question generation from paragraphs . they augment a sequence-to-sequence QG model with dynamic, paragraph-specific dictionary . |
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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. |
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Open Domain Question Answering over Tables via Dense Retrieval (2021.naacl-main)
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| Challenge: | Recent advances in open-domain QA focus on retrieving textual passages . a retriever designed to handle tabular context can improve retrieval quality . |
| Approach: | They propose a tabular-based retrieval model that improves retrieval quality over a BERT-based retriever. |
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A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection (2020.coling-main)
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| Challenge: | Existing approaches to QA re-rank sentences use huge neural models or complex attentive architectures. |
| Approach: | They propose to exploit the intrinsic structure of the original rank with an effective word-relatedness encoder to achieve the highest accuracy among the cost-efficient models. |
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