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This paper addresses the question of identifying the best candidate answer toa question on Community Question Answer (CQA) forums. The problem is importantbecause Individuals often visit CQA forums to seek answers to nuancedquestions. We develop a novel induced relational graph convolutional network(IR-GCN) framework to address the question. We make three contributions. First,we introduce a modular framework that separates the construction of the graphwith the label selection mechanism. We use equivalence relations to induce agraph comprising cliques and identify two label assignment mechanisms---labelcontrast, label sharing. Then, we show how to encode these assignmentmechanisms in GCNs. Second, we show that encoding contrast createsdiscriminative magnification---enhancing the separation between nodes in theembedding space. Third, we show a surprising result---boosting techniquesimprove learning over familiar stacking, fusion, or aggregation approaches forneural architectures. We show strong results over the state-of-the-art neuralbaselines in extensive experiments on 50 StackExchange communities.

An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms