DeepAI AI Chat
Log In Sign Up

Exploiting Rich Syntax for Better Knowledge Base Question Answering

by   Pengju Zhang, et al.
Tencent QQ
Soochow University
NetEase, Inc

Recent studies on Knowledge Base Question Answering (KBQA) have shown great progress on this task via better question understanding. Previous works for encoding questions mainly focus on the word sequences, but seldom consider the information from syntactic trees.In this paper, we propose an approach to learn syntax-based representations for KBQA. First, we encode path-based syntax by considering the shortest dependency paths between keywords. Then, we propose two encoding strategies to mode the information of whole syntactic trees to obtain tree-based syntax. Finally, we combine both path-based and tree-based syntax representations for KBQA. We conduct extensive experiments on a widely used benchmark dataset and the experimental results show that our syntax-aware systems can make full use of syntax information in different settings and achieve state-of-the-art performance of KBQA.


page 1

page 2

page 3

page 4


Syntax-aware Neural Semantic Role Labeling

Semantic role labeling (SRL), also known as shallow semantic parsing, is...

Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base

Knowledge-based visual question answering (KVQA) task aims to answer que...

Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling

The goal of semantic role labelling (SRL) is to recognise the predicate-...

Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge

In this paper, we propose to enhance the pair-wise aspect and opinion te...

CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error Correction with a Tailored GEC-Oriented Parser

Recently, Zhang et al. (2022) propose a syntax-aware grammatical error c...

Deep RNNs Encode Soft Hierarchical Syntax

We present a set of experiments to demonstrate that deep recurrent neura...

Representing Syntax and Composition with Geometric Transformations

The exploitation of syntactic graphs (SyGs) as a word's context has been...