Adversarial Training Improved Multi-Path Multi-Scale Relation Detector for Knowledge Base Question Answering

doi:10.1109/ACCESS.2020.2984393

Adversarial Training Improved Multi-Path Multi-Scale Relation Detector for Knowledge Base Question Answering

2020-01-01

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DOI：10.1109/ACCESS.2020.2984393

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Knowledge Base Question Answering (KBQA) is a promising approach for users to access substantial knowledge and has become a research focus in recent years. Our paper focuses on relation detection, a subtask of KBQA and proposes an adversarial training improved multi-path multi-scale relation detector (AdvT-MMRD) to improve the performance of a common KBQA system. To solve the problem of matching the casual form of a question with the logical form of a predicate, we use question pattern-relation matching, in which an attention-based bidirectional recurrent neural network with gated recurrent units (Bi-GRUs) is used to match semantic similarity and a convolutional neural network (CNN) is used to learn literal similarity between question and relation. We also explore two ways to measure the relevance of entity type-relation pairs through several level representations. Additionally, an adversarial training strategy is conducted to enhance our model. The experimental results demonstrate that our approach not only achieves a state-of-the-art accuracy of 93.8 & x0025; on relation detection task, but contributes our KBQA system to reaching an outstanding accuracy of 79.0 & x0025; on the SimpleQuestions benchmark.

Keywords:

Adversarial training

knowledge base question answering

relation detection

Adversarial Training Improved Multi-Path Multi-Scale Relation Detector for Knowledge Base Question Answering

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