Purpose

Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.

Design/methodology/approach

This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.

Findings

The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.

Originality/value

This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

中文翻译：

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面向领域问答系统问题意图检测与槽填充联合建模方法

目的

意图检测和槽填充是问答系统问题理解中的两个重要任务。本研究旨在建立一个具有一定泛化能力的联合任务模型，并将其性能与本文提到的其他神经网络模型进行基准测试。

设计/方法论/途径

本研究使用基于深度学习的方法对问题意图检测和槽填充进行联合建模。同时，改进了长短期记忆（LSTM）网络的内部单元结构。此外，基于科学技术知识图构建了计算机科学文献问题（CSLQ）数据集。使用Airline Travel Information Systems、Snips（Snips收集的消费者意图引擎的自然语言处理数据集）和CSLQ数据集进行实证分析。比较了几种模型的意图检测准确性和槽位填充的F 1 分数以及句子的语义准确性。

发现

结果表明，所提出的模型优于所有其他基准方法，特别是对于 CSLQ 数据集。这证明本研究的设计在一定程度上提高了模型的综合性能和泛化能力。

原创性/价值

这项研究有助于理解特定领域的问句。对LSTM进行改进，构建计算机文献领域数据集。这将为未来构建计算机文学问答系统奠定数据和模型基础。