Emotional support conversation aims at reducing the emotional distress of the
help-seeker, which is a new and challenging task. It requires the system to
explore the cause of help-seeker's emotional distress and understand their
psychological intention to provide supportive responses. However, existing
methods mainly focus on the sequential contextual information, ignoring the
hierarchical relationships with the global cause and local psychological
intention behind conversations, thus leads to a weak ability of emotional
support. In this paper, we propose a Global-to-Local Hierarchical Graph Network
to capture the multi-source information (global cause, local intentions and
dialog history) and model hierarchical relationships between them, which
consists of a multi-source encoder, a hierarchical graph reasoner, and a
global-guide decoder. Furthermore, a novel training objective is designed to
monitor semantic information of the global cause. Experimental results on the
emotional support conversation dataset, ESConv, confirm that the proposed GLHG
has achieved the state-of-the-art performance on the automatic and human
evaluations. The code will be released in here
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本文提出了一种全局到局部的分层图网络方法，用于情感支持对话任务中多源信息的建模，同时监测全局因素的语义信息，实现了该任务的最新最优效果。