| 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 . |
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Multi-Domain Multilingual Question Answering (2021.emnlp-tutorials)
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| Challenge: | Question answering (QA) is one of the most challenging tasks in natural language processing. |
| Approach: | a tutorial examines the state-of-the-art approaches to multi-domain and multilingual QA . they introduce standard benchmarks and discuss out-of the-box training with open-domain QA systems . |
| Outcome: | This tutorial aims to bridge the gap between open-domain and multilingual QA. |
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. |
A Survey on Open Information Extraction (C18-1)
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| Challenge: | Existing approaches to open information extraction (Open IE) focus on narrow, well-defined requests over a predefined set of target relations on small, homogeneous corpora. |
| Approach: | They propose to use unsupervised methods to extract all types of relations found in text . they propose to implement a system that can be automated to detect possible relations . |
| Outcome: | The proposed approaches have been compared with existing methods and are based on the results of a literature review. |
Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation (2026.acl-long)
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| Challenge: | Table Question Answering (TQA) aims to answer natural language questions using tabular data. |
| Approach: | They propose a systematic overview of TQA research using large language models and summarize available benchmarks based on task features. |
| Outcome: | The proposed framework provides a comprehensive overview of the current state of the art in the field of Table Question Answering. |
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. |
UnitedQA: A Hybrid Approach for Open Domain Question Answering (2021.acl-long)
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| Challenge: | Recent work on open-domain question answering focuses on either extractive or generative readers exclusively. |
| Approach: | They propose a hybrid approach to extractive and generative readers that leverages both models. |
| Outcome: | The proposed approach outperforms state-of-the-art models on NaturalQuestions and TriviaQA respectively. |
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. |
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering (D19-58)
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| Challenge: | Existing work on open-domain multi-hop question answering relies on off-the-shelf information retrieval techniques to retrieve answer passages. |
| Approach: | They propose a new subproblem for open-domain multi-hop question answering . they aim to recognize the anchor from a set of start passages with a reading comprehension model . |
| Outcome: | The proposed method significantly improves the baseline method on the open-domain hotpotQA benchmark. |
Answering Open-Domain Questions of Varying Reasoning Steps from Text (2021.emnlp-main)
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| Challenge: | a new benchmark is developed to answer open-domain questions from text . the system uses a single multi-task transformer model to perform all the necessary subtasks . |
| Approach: | They develop a unified system to answer directly from open-domain questions . they use a single multi-task transformer model to perform all the necessary subtasks . |
| Outcome: | The proposed system can answer open-domain questions on any text collection without prior knowledge of reasoning complexity. |
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. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model by 1.0 and 0.7 exact match scores on the Natural Questions and TriviaQA open datasets. |