| Challenge: | Open domain question answering (ODQA) is a longstanding task that can answer factoid questions without explicit evidence in natural language processing (NLP). |
| Approach: | They propose to use open domain question answering to answer factual questions from a large knowledge corpus without explicit evidence. |
| Outcome: | The proposed models can answer factoid questions from a large knowledge corpus without explicit evidence. |
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FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection (2024.acl-long)
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| Challenge: | Open Domain Question Answering (ODQA) is a longstanding task in Natural Language Processing that involves generating an answer solely based on a given question. |
| Approach: | They propose a novel approach that executes sentence selection on the encoded passages to enhance the inference speed while reducing the context length required for generating answers. |
| Outcome: | The proposed approach can increase inference speed by **2.3X-5.7X** while maintaining the model’s performance. |
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. |
| Approach: | They propose a benchmark to evaluate ODQA's domain robustness using Wikipedia corpus . they annotate QA pairs in retrieval datasets with rigorous quality control . |
| Outcome: | The proposed benchmark improves model performance on annotated QA pairs in retrieval datasets with rigorous quality control. |
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study (2021.tacl-1)
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| Challenge: | Recent advances in open-domain question answering (ODQA) have led to human-level performance on many datasets. |
| Approach: | They provide a comprehensive and quantitative analysis about the difficulty of book QA . they compare the results of their research with extensive ODQA experiments . |
| Outcome: | The proposed model outperforms existing models on event-oriented questions on the NarrativeQA dataset. |
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 . |
Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization (2024.findings-naacl)
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| Challenge: | Open-domain Question Answering (OpenQA) aims at answering factual questions using an external large-scale knowledge corpus. |
| Approach: | They propose a retrieval-augmented approach to QA that focuses on retrieving relevant knowledge from an external corpus. |
| Outcome: | The proposed model can generalize to completely different knowledge domains while adapting to updated versions of the same knowledge corpus and switching to completely new knowledge domain. |
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. |
Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge (2022.findings-emnlp)
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| Challenge: | Existing open-domain question answering methods rely on the retriever to gather all evidence in isolation, but our approach uses an intermediary module to perform a chain of reasoning over the retrieved set. |
| Approach: | They propose a new open-domain question answering framework that integrates an intermediary module into the current retriever-reader pipeline and integrates it into the model. |
| Outcome: | The proposed framework outperforms the state-of-the-art on two OTT-QA datasets with an exact match score of 47.3 (45% relative gain). |
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. |
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. |
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. |
| Outcome: | The proposed retriever improves retrieval quality with mined hard negatives over a BERT-based retriever. |