Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
This work addresses knowledge base question answering for users needing accurate answers from structured data, representing an incremental improvement with a novel hybrid approach.
The paper tackled the problem of answering natural language questions over knowledge bases by modeling subtle inter-relationships between questions and KB aspects, resulting in a method that significantly outperforms information-retrieval based methods and remains competitive with semantic parsing based methods on the WebQuestions benchmark.
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.