Commonsense knowledge is crucial to many natural language processing tasks.
Existing works usually incorporate graph knowledge with conventional graph
neural networks (GNNs), leading to the text and graph knowledge encoding
processes being separated in a serial pipeline. We argue that these separate
representation learning stages may be suboptimal for neural networks to learn
the overall context contained in both types of input knowledge. In this paper,
we propose a novel context-aware graph-attention model (Context-aware GAT),
which can effectively incorporate global features of relevant knowledge graphs
based on a context-enhanced knowledge aggregation process. Specifically, our
framework leverages a novel representation learning approach to process
heterogeneous features - combining flattened graph knowledge with text. To the
best of our knowledge, this is the first attempt at hierarchically applying
graph knowledge aggregation on a connected subgraph in addition to contextual
information to support commonsense dialogue generation. This framework shows
superior performance compared to conventional GNN-based language frameworks.
Both automatic and human evaluation demonstrates that our proposed model has
significant performance uplifts over state-of-the-art baselines.

本文提出一种基于上下文感知的图注意力模型 (Context-aware GAT)，可以在知识聚合流程中有效地整合相关知识图的全局特征，并且优于传统基于图神经网络 (GNNs) 的语言框架。

CADGE：基于图结构知识聚合的上下文感知对话生成

CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured  Knowledge Aggregation

In previous research, knowledge-selection tasks mostly rely on language
model-based methods or knowledge ranking. However, while approaches that rely
on the language models take all knowledge as sequential input, knowledge does
not contain sequential information in most circumstances. On the other hand,
the knowledge-ranking methods leverage dialog history and each given knowledge
snippet separately, but they do not consider information between knowledge
snippets. In the Tenth Dialog System Technology Challenges (DSTC10), we
participated in the second Knowledge-grounded Task-oriented Dialogue Modeling
on Spoken Conversations. To deal with the problems mentioned above, we modified
training methods based on state-of-the-art (SOTA) models for the first and
third sub-tasks. As for the second sub-task of knowledge selection, we proposed
Graph-Knowledge Selector (GKS), utilizing a graph-attention base model
incorporated with the language model. GKS makes knowledge-selection decisions
in the dialog by simultaneously considering each knowledge embedding generated
from the language model without sequential features. Moreover, GKS leverages
considerable knowledge in decision-making and takes relations across knowledge
as part of the selection process. As a result, GKS outperforms several SOTA
models proposed in the data-set on knowledge selection from the Ninth Dialog
System Technology Challenges (DSTC9).

本论文基于当代最先进的语言模型构建了 Graph-Knowledge Selector（GKS）来解决对话系统中知识选择的问题，并考虑了知识之间的联系，明显优于 Ninth Dialog System Technology Challenges（DSTC9）中提出的几种最新技术模型。