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An intention multiple-representation model with expanded information

机译：具有扩展信息的意图多表示模型

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摘要

Short text is the main carrier for people to express their ideas and opinions. And it is very important as well a big challenge to understand the meaning of short text or recognize the semantic patterns of different short texts. Most of existing methods use word embedding and short text interaction to learn short text pairs semantic patterns. However, some of these methods are complicated and cannot fully capture the relations of words and the interaction of short text pairs. To solve this problem, a self-attention based model, that is, Knowledge Learning for Matching Question (KLMQ) is proposed. Using part-of-speech to mine the relations of words, and the model obtains the relations of short texts from the grammar, syntax and morphology. Meanwhile, it adopts information fusion strategy to enhance the interaction between short text pairs, which ensures the model works well with the expanded information, such as the order of words, the correlation of words and the relations of short text pairs. To verify the correctness of the proposed model and the efficiency of the expanded information, extensive experiments were carried out on public datasets. Experimental results show that the model performs better than that of traditional neural network models and the expanded information can much improve the performances of intention multiple-representation recognition.
机译：短文本是人们表达他们的想法和意见的主要承运人。并且了解短文本的含义或识别不同短文本的语义模式是非常重要的。大多数现有方法使用Word嵌入和短文本交互来学习短文本对语义模式。然而，其中一些方法复杂，无法完全捕捉单词的关系和短文本对的交互。为了解决这个问题，提出了一种基于自我关注的模型，即匹配问题（KLMQ）的知识学习。使用言语部分来挖掘单词的关系，并且该模型从语法，语法和形态学获得短文本的关系。同时，它采用信息融合策略来增强短文本对之间的交互，这确保了模型与扩展信息良好，例如单词顺序，单词的相关性和短文本对的关系。为了验证所提出的模型的正确性和扩展信息的效率，在公共数据集上进行了广泛的实验。实验结果表明，该模型比传统的神经网络模型更好，扩展信息可以大大提高意图多呈现识别的性能。

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  • 来源
    《Computer speech and language》 |2021年第7期|101196.1-101196.12|共12页
  • 作者

    Jingxiang Hu; Junjie Peng; Wenqiang Zhang; Lizhe Qi; Miao Hu; Huanxiang Zhang;

  • 作者单位

    School of Computer Engineering and Science Shanghai University Shanghai China;

    School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China;

    Academy for Engineering & Technology Fudan University Shanghai China School of Computer Science and Technology Fudan University Shanghai China;

    Academy for Engineering & Technology Fudan University Shanghai China;

    School of Computer Engineering and Science Shanghai University Shanghai China;

    School of Computer Engineering and Science Shanghai University Shanghai China;

  • 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

    Intention multiple-representation; Intention recognition; Information fusion; Knowledge learning; Self-attention;

    机译：意图多重代表;意图识别;信息融合;知识学习;自我关注;

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