# Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases

Source: [Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases](<https://www.sota2.com/research/paper/68fa65da1e209131cea0cc8f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:36:37.771Z

Authors: Yu Chen, Lingfei Wu, Mohammed J. Zaki

Published: 2019-03-06T06:09:02.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1903.02188>)

[PDF](<https://arxiv.org/pdf/1903.02188.pdf>)