  The nonliteral interpretation of a text is hard to be understood by machine
models due to its high context-sensitivity and heavy usage of figurative
language. In this study, inspired by human reading comprehension, we propose a
novel, simple, and effective deep neural framework, called Skim and Intensive
Reading Model (SIRM), for figuring out implied textual meaning. The proposed
SIRM consists of two main components, namely the skim reading component and
intensive reading component. N-gram features are quickly extracted from the
skim reading component, which is a combination of several convolutional neural
networks, as skim (entire) information. An intensive reading component enables
a hierarchical investigation for both local (sentence) and global (paragraph)
representation, which encapsulates the current embedding and the contextual
information with a dense connection. More specifically, the contextual
information includes the near-neighbor information and the skim information
mentioned above. Finally, besides the normal training loss function, we employ
an adversarial loss function as a penalty over the skim reading component to
eliminate noisy information arisen from special figurative words in the
training data. To verify the effectiveness, robustness, and efficiency of the
proposed architecture, we conduct extensive comparative experiments on several
sarcasm benchmarks and an industrial spam dataset with metaphors. Experimental
results indicate that (1) the proposed model, which benefits from context
modeling and consideration of figurative language, outperforms existing
state-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 final
framework is efficient enough.
