9,258 research outputs found
NCGNN: Node-level Capsule Graph Neural Network
Message passing has evolved as an effective tool for designing Graph Neural
Networks (GNNs). However, most existing works naively sum or average all the
neighboring features to update node representations, which suffers from the
following limitations: (1) lack of interpretability to identify crucial node
features for GNN's prediction; (2) over-smoothing issue where repeated
averaging aggregates excessive noise, making features of nodes in different
classes over-mixed and thus indistinguishable. In this paper, we propose the
Node-level Capsule Graph Neural Network (NCGNN) to address these issues with an
improved message passing scheme. Specifically, NCGNN represents nodes as groups
of capsules, in which each capsule extracts distinctive features of its
corresponding node. For each node-level capsule, a novel dynamic routing
procedure is developed to adaptively select appropriate capsules for
aggregation from a subgraph identified by the designed graph filter.
Consequently, as only the advantageous capsules are aggregated and harmful
noise is restrained, over-mixing features of interacting nodes in different
classes tends to be avoided to relieve the over-smoothing issue. Furthermore,
since the graph filter and the dynamic routing identify a subgraph and a subset
of node features that are most influential for the prediction of the model,
NCGNN is inherently interpretable and exempt from complex post-hoc
explanations. Extensive experiments on six node classification benchmarks
demonstrate that NCGNN can well address the over-smoothing issue and
outperforms the state of the arts by producing better node embeddings for
classification
Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network
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.Comment: Accepted by CIKM 2020 (Long Paper
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
A capsule is a group of neurons, whose activity vector represents the
instantiation parameters of a specific type of entity. In this paper, we
explore the capsule networks used for relation extraction in a multi-instance
multi-label learning framework and propose a novel neural approach based on
capsule networks with attention mechanisms. We evaluate our method with
different benchmarks, and it is demonstrated that our method improves the
precision of the predicted relations. Particularly, we show that capsule
networks improve multiple entity pairs relation extraction.Comment: To be published in EMNLP 201
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