Complex Factoid Question Answering with a Free-Text Knowledge Graph
- URL: http://arxiv.org/abs/2103.12876v1
- Date: Tue, 23 Mar 2021 22:53:09 GMT
- Title: Complex Factoid Question Answering with a Free-Text Knowledge Graph
- Authors: Chen Zhao, Chenyan Xiong, Xin Qian and Jordan Boyd-Graber
- Abstract summary: DELFT builds a free-text knowledge graph from Wikipedia.
For each question, DELFT finds the subgraph linking question entity nodes to candidates using text sentences as edges.
A novel graph neural network reasons over the free-text graph-combining evidence on the nodes via information along edge sentences to select a final answer.
- Score: 20.889798402634323
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce DELFT, a factoid question answering system which combines the
nuance and depth of knowledge graph question answering approaches with the
broader coverage of free-text. DELFT builds a free-text knowledge graph from
Wikipedia, with entities as nodes and sentences in which entities co-occur as
edges. For each question, DELFT finds the subgraph linking question entity
nodes to candidates using text sentences as edges, creating a dense and high
coverage semantic graph. A novel graph neural network reasons over the
free-text graph-combining evidence on the nodes via information along edge
sentences-to select a final answer. Experiments on three question answering
datasets show DELFT can answer entity-rich questions better than machine
reading based models, bert-based answer ranking and memory networks. DELFT's
advantage comes from both the high coverage of its free-text knowledge
graph-more than double that of dbpedia relations-and the novel graph neural
network which reasons on the rich but noisy free-text evidence.
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