PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text (D19-1)
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| Challenge: | Experimentally PullNet improves over the prior state-of-the-art open domain question answering systems. |
| Approach: | They propose a framework for learning what to retrieve and reasoning with heterogeneous information to find the best answer. |
| Outcome: | The proposed framework improves over the prior state-of-the-art in open domain question answering . it is weakly supervised, requiring question-answer pairs but not gold inference paths . |
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Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text (D18-1)
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| Challenge: | Specialized neural models have been developed for extracting answers from text alone or Knowledge Bases (KBs) alone. |
| Approach: | They propose a novel model for extracting answers from a question-specific subgraph containing text and KB entities and relations. |
| Outcome: | The proposed model outperforms existing methods in a combination of a KB and entity-linked text in QA over a large text corpus. |
Open-Domain Question Answering with Pre-Constructed Question Spaces (2021.naacl-srw)
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| Challenge: | Open-domain question answering aims at locating answers to user-generated questions in massive collections of documents. |
| Approach: | They propose an algorithm with a novel reader-retriever design that differs from both families of algorithms. |
| Outcome: | The proposed algorithm outperforms retrieval-based methods with two large-scale datasets and is state-of-the-art. |
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 . |
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Answering Complex Open-domain Questions Through Iterative Query Generation (D19-1)
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| Challenge: | Currently, one-step retrieve-and-read question answering systems cannot answer such questions because they rarely contain retrievable clues about the missing entity. |
| Approach: | They propose a multi-step approach to retrieve relevant content with the question, then reading the paragraphs returned by the information retrieval component to arrive at the final answer. |
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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. |
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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 . |
| 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 . |
iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering (2025.acl-long)
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| Challenge: | Large language models suffer from factual inaccuracies in knowledge-intensive domains. |
| Approach: | They propose a question-guided KBQA framework that iteratively decomposes complex queries into simpler sub-questions and integrates a Graph Neural Network (GNN) to look ahead and incorporate 2-hop neighbor information at each reasoning step. |
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C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References (2022.acl-short)
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| Challenge: | Existing approaches to pretrain open-domain question answering systems lack task-specific annotations. |
| Approach: | They propose to pretrain a two-stage open-domain question answering system with strong transfer capabilities by using a dictionary and a large-scale corpus. |
| Outcome: | The proposed approach leads to 2%-10% gains in top-20 accuracy and improves with reader. |
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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DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
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| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
| Approach: | They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation . |
| Outcome: | The proposed model outperforms previous approaches by a significant margin in QA tasks over text. |