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Hierarchical state recurrent neural network for social emotion ranking

机译：社会情感排名的分层状态经常性神经网络

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摘要

Text generation with auxiliary attributes, such as topics or sentiments, has made remarkable progress. However, high-quality labeled data is difficult to obtain for the large-scale corpus. Therefore, this paper focuses on social emotion ranking aiming to identify social emotions with different intensities evoked by online documents, which could be potentially beneficial for further controlled text generation. Existing studies often consider each document as an entirety that fail to capture the inner relationship between sentences in a document. In this paper, we propose a novel hierarchical state recurrent neural network for social emotion ranking. A hierarchy mechanism is employed to capture the key hierarchical semantic structure in a document. Moreover, instead of incrementally reading a sequence of words or sentences as in traditional recurrent neural networks, the proposed approach encodes the hidden states of all words or sentences simultaneously at each recurrent step to capture long-range dependencies precisely. Experimental results show that the proposed approach performs remarkably better than the state-of-the-art social emotion ranking approaches and is useful for controlled text generation.
机译：具有辅助属性的文本生成，例如主题或情绪，取得了显着的进展。但是，对于大规模语料库，难以获得高质量的标记数据。因此，本文侧重于社会情感排名，旨在识别在线文件引起的不同强度的社会情绪，这可能对进一步控制的文本生成有益。现有研究通常会将每个文档视为一种整体，无法捕获文档中的句子之间的内部关系。在本文中，我们提出了一种用于社会情感排名的新型等级经常性神经网络。使用层次结构机制来捕获文档中的密钥分层语义结构。此外，而不是以传统的经常性神经网络中的一种单词或句子逐步读取一系列单词或句子，而是在每个反复步骤中同时对所有单词或句子的隐藏状态进行编码，以精确地捕获远程依赖性。实验结果表明，该方法比艺术最先进的社会情感排名方法更好地表现出显着更好，对受控文本生成有用。

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  • 来源
    《Computer speech and language》 |2021年第7期|101177.1-101177.14|共14页
  • 作者

    Deyu Zhou; Meng Zhang; Yang Yang; Yulan He;

  • 作者单位

    School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China;

    School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China;

    School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China;

    Department of Computer Science University of Warwick UK;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Sentiment analysis; Social emotion ranking; Attention mechanism;

    机译：情绪分析;社会情感排名;注意机制;

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