Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks (N18-2)
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| Challenge: | Existing work on simple question answering over knowledge graphs involves increasingly complex NN architectures. |
| Approach: | They propose to decompose the problem into entity detection, entity linking, relation prediction, evidence combination and heuristics. |
| Outcome: | The proposed approach outperforms existing models and benchmarks on a simple QA task. |
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Simple Question Answering with Subgraph Ranking and Joint-Scoring (N19-1)
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| Challenge: | Knowledge graph based simple question answering is a major area of research in question answering. |
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| Challenge: | a recent demonstration of the power of huge language models such as GPT-2 to memorise the answers to factoid questions raises questions about the extent to which knowledge is embedded directly within these large models. |
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Neural Ranking with Weak Supervision for Open-Domain Question Answering : A Survey (2023.findings-eacl)
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A System for Answering Simple Questions in Multiple Languages (2023.acl-demo)
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| Challenge: | Existing knowledge graph question answering systems are limited to simple questions, but they can be used to answer complex questions. |
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PerKGQA: Question Answering over Personalized Knowledge Graphs (2022.findings-naacl)
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| Challenge: | Existing methods for question answering over knowledge graphs have focused on generalizable or generic knowledge, which assumes there is a predefined global KG for all queries. |
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