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Article overview
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Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network | | Junyi Li
; Siqing Li
; Wayne Xin Zhao
; Gaole He
; Zhicheng Wei
; Nicholas Jing Yuan
; Ji-Rong Wen
; | | Date: |
4 Oct 2020 | | Abstract: | Personalized review generation (PRG) aims to automatically produce review
text reflecting user preference, which is a challenging natural language
generation task. Most of previous studies do not explicitly model factual
description of products, tending to generate uninformative content. Moreover,
they mainly focus on word-level generation, but cannot accurately reflect more
abstractive user preference in multiple aspects. To address the above issues,
we propose a novel knowledge-enhanced PRG model based on capsule graph neural
network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG)
for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules
for encoding underlying characteristics from the HKG. Our generation process
contains two major steps, namely aspect sequence generation and sentence
generation. First, based on graph capsules, we adaptively learn aspect capsules
for inferring the aspect sequence. Then, conditioned on the inferred aspect
label, we design a graph-based copy mechanism to generate sentences by
incorporating related entities or words from HKG. To our knowledge, we are the
first to utilize knowledge graph for the PRG task. The incorporated KG
information is able to enhance user preference at both aspect and word levels.
Extensive experiments on three real-world datasets have demonstrated the
effectiveness of our model on the PRG task. | | Source: | arXiv, 2010.01480 | | Services: | Forum | Review | PDF | Favorites | |
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