Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base

TL;DR

Crake introduces a causal-enhanced table-filler approach for KBQA that models causal relationships between subtasks, improving accuracy and scalability over previous methods.

Contribution

The paper proposes a novel causal-enhanced table-filler for semantic parsing, explicitly modeling causalities between node extraction and graph composition in KBQA.

Findings

Surpasses previous state-of-the-art by 17% on LC-QuAD 1.0

Efficiently scales to large-scale knowledge bases

Maintains time and space efficiency

Abstract

Semantic parsing solves knowledge base (KB) question answering (KBQA) by composing a KB query, which generally involves node extraction (NE) and graph composition (GC) to detect and connect related nodes in a query. Despite the strong causal effects between NE and GC, previous works fail to directly model such causalities in their pipeline, hindering the learning of subtask correlations. Also, the sequence-generation process for GC in previous works induces ambiguity and exposure bias, which further harms accuracy. In this work, we formalize semantic parsing into two stages. In the first stage (graph structure generation), we propose a causal-enhanced table-filler to overcome the issues in sequence-modelling and to learn the internal causalities. In the second stage (relation extraction), an efficient beam-search algorithm is presented to scale complex queries on large-scale KBs. Experiments on LC-QuAD 1.0 indicate that our method surpasses previous state-of-the-arts by a large margin (17%) while remaining time and space efficiency. The code and models are available at https://github.com/AOZMH/Crake.