阅读超越线条：通过略读和精读模式理解语篇隐含意义
Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model
摘要：
文本的非字面解释由于其高度的上下文敏感性和大量的比喻性语言的使用，很难被机器模型理解。在这项研究中，受人类阅读理解的启发，我们提出了一个新颖，简单，有效的深层神经框架，称为略读和精读模型（SIRM），用于找出隐含的文本意义。建议的SIRM包括两个主要部分，即略读部分和精读部分。从略读组件中快速提取N-gram特征作为略读（整个）信息，略读组件是几个卷积神经网络的组合。一个精读组件，使本地（句子）和全球（段落）表示，它封装了当前的嵌入和上下文信息与密集的连接的分层调查。更具体地，上下文信息包括上述近邻信息和略读信息。最后，除了正常的训练损失函数，我们采用了对抗性的损失函数作为对略读部分的惩罚，以消除噪音信息所产生的特殊比喻词在训练数据。为了验证所提出的架构的有效性，鲁棒性和效率，我们进行了广泛的比较实验上的讽刺基准和工业垃圾邮件数据集的隐喻。实验结果表明：（1）该模型在参数规模和训练速度方面优于现有的最新解决方案，具有可比的参数规模和训练速度;（2）SIRM在参数大小敏感性方面具有更好的鲁棒性;（3）与SIRM的切除和添加变体相比，最终框架足够有效。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
