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The nonliteral interpretation of a text is hard to be understood by machinemodels due to its high context-sensitivity and heavy usage of figurativelanguage. In this study, inspired by human reading comprehension, we propose anovel, simple, and effective deep neural framework, called Skim and IntensiveReading Model (SIRM), for figuring out implied textual meaning. The proposedSIRM consists of two main components, namely the skim reading component andintensive reading component. N-gram features are quickly extracted from theskim reading component, which is a combination of several convolutional neuralnetworks, as skim (entire) information. An intensive reading component enablesa hierarchical investigation for both local (sentence) and global (paragraph)representation, which encapsulates the current embedding and the contextualinformation with a dense connection. More specifically, the contextualinformation includes the near-neighbor information and the skim informationmentioned above. Finally, besides the normal training loss function, we employan adversarial loss function as a penalty over the skim reading component toeliminate noisy information arisen from special figurative words in thetraining data. To verify the effectiveness, robustness, and efficiency of theproposed architecture, we conduct extensive comparative experiments on severalsarcasm benchmarks and an industrial spam dataset with metaphors. Experimentalresults indicate that (1) the proposed model, which benefits from contextmodeling and consideration of figurative language, outperforms existingstate-of-the-art solutions, with comparable parameter scale and training speed;(2) the SIRM yields superior robustness in terms of parameter size sensitivity;(3) compared with ablation and addition variants of the SIRM, the finalframework is efficient enough.

Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model