This paper addresses the question of identifying the best candidate answer to a question on Community Question Answer (CQA) forums. The problem is important because Individuals often visit CQA forums to seek answers to nuanced questions. 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 graph with the label selection mechanism. We use equivalence relations to induce a graph comprising cliques and identify two label assignment mechanisms---label contrast, label sharing. Then, we show how to encode these assignment mechanisms in GCNs. Second, we show that encoding contrast creates discriminative magnification---enhancing the separation between nodes in the embedding space. Third, we show a surprising result---boosting techniques improve learning over familiar stacking, fusion, or aggregation approaches for neural architectures. We show strong results over the state-of-the-art neural baselines in extensive experiments on 50 StackExchange communities.
@article{arxiv.1911.06957,
title = {An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms},
author = {Kanika Narang and Chaoqi Yang and Adit Krishnan and Junting Wang and Hari Sundaram and Carolyn Sutter},
journal= {arXiv preprint arXiv:1911.06957},
year = {2019}
}