| 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. |
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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. |
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 . |
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RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering (2023.findings-acl)
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Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Yang Wang, Zhiheng Huang, Bing Xiang, Dan Roth
| Challenge: | Existing ODQA datasets consist mainly of Wikipedia corpus, and are insufficient to study models’ generalizability across diverse domains. |
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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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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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A Survey for Efficient Open Domain Question Answering (2023.acl-long)
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| Challenge: | Open domain question answering (ODQA) is a longstanding task that can answer factoid questions without explicit evidence in natural language processing (NLP). |
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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. |
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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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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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Denoising Table-Text Retrieval for Open-Domain Question Answering (2024.lrec-main)
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| Challenge: | Existing studies in table-text open-domain question answering have problems with false-positive labels in training datasets. |
| Approach: | They propose a denoised table-text retriever that discards false positives from training datasets . they integrate table-level ranking information into the retriever to assist in finding evidence . |
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