graph convolutional network

Sarcasm is a sophisticated linguistic phenomenon that is prevalent on today’s social media platforms. Multi-modal sarcasm detection aims to identify whether a given sample with multi-modal information (i.e., text and image) is sarcastic. This task’s key lies in capturing both inter- and intra-modal incongruities within the same context. Although existing methods have achieved compelling success, they are disturbed by irrelevant information extracted from the whole image or text, or overlooking some important information due to the incomplete input. To address these limitations, we propose a Mutual-enhanced Incongruity Learning Network for multi-modal sarcasm detection, named MILNet. In particular, we design a local semantic-guided incongruity learning module and a global incongruity learning module. Moreover, we introduce a mutual enhancement module to take advantage of the underlying consistency between the two modules to boost the performance. Extensive experiments on a widely-used dataset demonstrate the superiority of our model over cutting-edge methods.

Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over-smoothing issue (indistinguishable representations of nodes in different classes). In this work, we present a systematic and quantitative study on the over-smoothing issue of GNNs. First, we introduce two quantitative metrics, MAD and MADGap, to measure the smoothness and over-smoothness of the graph nodes representations, respectively. Then, we verify that smoothing is the nature of GNNs and the critical factor leading to over-smoothness is the low information-to-noise ratio of the message received by the nodes, which is partially determined by the graph topology. Finally, we propose two methods to alleviate the over-smoothing issue from the topological view: (1) MADReg which adds a MADGap-based regularizer to the training objective;(2) AdaGraph which optimizes the graph topology based on the model predictions. Extensive experiments on 7 widely-used graph datasets with 10 typical GNN models show that the two proposed methods are effective for relieving the over-smoothing issue, thus improving the performance of various GNN models.