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Graph Enhanced Memory Networks for Sentiment Analysis

Zhao Xu; Romain Vial; Kristian Kersting
In: Michelangelo Ceci; Jaakko Hollmén; Ljupco Todorovski; Celine Vens; Saso Dzeroski (Hrsg.). Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD-2017), September 18-22, Skopje, Macedonia, The Former Yugoslav Republic of, Pages 374-389, Lecture Notes in Computer Science, Vol. 10534, Springer, 2017.


Memory networks model information and knowledge as memories that can be manipulated for prediction and reasoning about questions of interest. In many cases, there exists complicated relational structure in the data, by which the memories can be linked together into graphs to propagate information. Typical examples include tree structure of a sentence and knowledge graph in a dialogue system. In this paper, we present a novel graph enhanced memory network GEMN to integrate relational information between memories for prediction and reasoning. Our approach introduces graph attentions to model the relations, and couples them with content-based attentions via an additional neural network layer. It thus can better identify and manipulate the memories related to a given question, and provides more accurate prediction about the final response. We demonstrate the effectiveness of the proposed approach with aspect based sentiment classification. The empirical analysis on real data shows the advantages of incorporating relational dependencies into the memory networks.

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