Papers › Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases

Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases

6 Mar 2019NAACL 2019 6arXiv:1903.02188archive 2025-07-28

Yu Chen, Lingfei Wu, Mohammed J. Zaki

When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles. However, most existing embedding-based methods for knowledge base question answering (KBQA) ignore the subtle inter-relationships between the question and the KB (e.g., entity types, relation paths and context). In this work, we propose to directly model the two-way flow of interactions between the questions and the KB via a novel Bidirectional Attentive Memory Network, called BAMnet. Requiring no external resources and only very few hand-crafted features, on the WebQuestions benchmark, our method significantly outperforms existing information-retrieval based methods, and remains competitive with (hand-crafted) semantic parsing based methods. Also, since we use attention mechanisms, our method offers better interpretability compared to other baselines.

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