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Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge bases.
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Chen Y, Wu L, Zaki M J. Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases[C]//Proceedings of NAACL-HLT. 2019: 2913-2923
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Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, and Jens Lehmann. 2019. Learning to rank query graphs for complex question answering over knowledge graphs. In International Semantic Web Conference. Springer, 487–504
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Xu K, Lai Y, Feng Y, et al. Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019: 2937-2947
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Liang C, Berant J, Le Q, et al. Neural symbolic machines: Learning semantic parsers on freebase with weak supervision[C]//55th Annual Meeting of the Association for Computational Linguistics, ACL 2017. Association for Computational Linguistics (ACL), 2017: 23-33
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Yu M, Yin W, Hasan K S, et al. Improved Neural Relation Detection for Knowledge Base Question Answering[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017: 571-581
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Talmor A, Berant J. The Web as a Knowledge-Base for Answering Complex Questions[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018: 641-651
2018
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Sun H, Dhingra B, Zaheer M, et al. Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 4231-4242
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Luo K, Lin F, Luo X, et al. Knowledge base question answering via encoding of complex query graphs[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 2185-2194
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Xu K, Wu L, Wang Z, et al. Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 918-924
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Sun H, Bedrax-Weiss T, Cohen W. PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing: 2380-2390
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2019
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Saxena A, Tripathi A, Talukdar P. Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 4498-4507
2020
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Qiu Y, Wang Y, Jin X, et al. Stepwise Reasoning for Multi-Relation Question Answering over Knowledge Graph with Weak Supervision[C]//Proceedings of the 13th International Conference on Web Search and Data Mining. 2020: 474-482
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Zhu S, Cheng X, Su S. Knowledge-based question answering by tree-to-sequence learning[J]. Neurocomputing, 2020, 372: 64-72
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Lan Y, Jiang J. Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 969-974
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2020
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Jia R, Liang P. Adversarial Examples for Evaluating Reading Comprehension Systems[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 2021-2031
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Trouillon T, Welbl J, Riedel S, et al. Complex Embeddings for Simple Link Prediction[C]//International Conference on Machine Learning. 2016: 2071-2080
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Hu S, Zou L, Zhang X. A state-transition framework to answer complex questions over knowledge base[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 2098-2108
2098
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