  Relation Extraction (RE) is to predict the relation type of two entities that
are mentioned in a piece of text, e.g., a sentence or a dialogue. When the
given text is long, it is challenging to identify indicative words for the
relation prediction. Recent advances on RE task are from BERT-based sequence
modeling and graph-based modeling of relationships among the tokens in the
sequence. In this paper, we propose to construct a latent multi-view graph to
capture various possible relationships among tokens. We then refine this graph
to select important words for relation prediction. Finally, the representation
of the refined graph and the BERT-based sequence representation are
concatenated for relation extraction. Specifically, in our proposed GDPNet
(Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph
Generator (GGG) to generate edges of the multi-view graph. The graph is then
refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we
show that GDPNet achieves the best performance on dialogue-level RE, and
comparable performance with the state-of-the-arts on sentence-level RE.
