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首页> 外文会议>Asia-Pacific Signal and Information Processing Association Annual Summit and Conference >User's Intention Understanding in Question-Answering System Using Attention-based LSTM
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User's Intention Understanding in Question-Answering System Using Attention-based LSTM

机译：使用基于注意力的LSTM的问答系统中的用户意图理解

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摘要

A rule-based question-answering system is limited in its ability to understand a user's intention due to the inevitable incompleteness of the rules. To address this problem, in this paper, we propose a method to estimate question type and question keyword class from a user's question by using an attention-based LSTM (Long Short-Term Memory) model. We also propose a joint model for simultaneous estimation of question type and question keyword class. Through the experiment, the effectiveness of our proposed method is evaluated based upon estimation rates. In addition, the proposed method for question type estimation is compared with a rule-based system, support vector machine (SVM), and Random Forest. The method for question keyword class estimation is also compared with the non-attention LSTM model and the conventional model.
机译：由于规则不可避免的不完整，基于规则的问答系统在理解用户意图方面的能力受到限制。为了解决这个问题，在本文中，我们提出了一种使用基于注意力的LSTM（长短期记忆）模型从用户的问题中估计问题类型和问题关键字类别的方法。我们还提出了一种联合模型，用于同时估计问题类型和问题关键字类别。通过实验，基于估计率对我们提出的方法的有效性进行了评估。此外，将提出的问题类型估计方法与基于规则的系统，支持向量机（SVM）和随机森林进行了比较。还将问题关键词类别估计的方法与非注意LSTM模型和常规模型进行了比较。

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