通过使用多通道CNN-BiLSTM多通道的语法和语义特征进行联合意图检测和插槽填充
Yusuf Idris Muhammad1, Naomie Salim1, Anazida Zainal1
1Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.
PeerJ. Computer science
|December 9, 2024
概括
本研究引入了一种用于自然语言理解 (NLU) 的新方法,该方法结合了多个文本嵌入来改善对话代理的意图检测和插槽填充. 混合方法显著提高了虚拟助理的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 对口语理解是对话代理的关键,意图检测和空格填充是自然语言理解 (NLU) 的核心任务.
- 对这些任务的联合学习显示出希望,但有限的数据阻碍了概括.
- 传统模型在语义和语法细微差别上扎,这对NLU至关重要.
研究的目的:
- 提出一种混合的文本表示,以加强联合意图检测和填空.
- 在使用有限的注释数据集的NLU模型中解决泛化挑战.
- 提高对话代理的准确性和效率.
主要方法:
- 一个多通道卷积神经网络 (CNN),集成非上下文 (word2vec),部分语音 (POS) 标签和上下文 (BERT) 嵌入.
- 共享双向长短期内存 (BiLSTM) 网络进程组合嵌入.
- 单独的软max分类器用于意图检测和插槽填充.
主要成果:
- 在ATIS数据集上实现了97.90%的意图准确度和98.86%的插槽填充F1分数.
- 在SNIPS数据集上实现了98.88%的意图准确度和97.07%的空格填充F1分数.
- 在这两组数据中表现明显优于基线模型.
结论:
- 拟议的混合文本表示有效地增强了联合意图检测和插槽填充.
- 该模型展示了卓越的性能,推进了对话系统.
- 在现实应用中为更准确,更高效的NLU铺平了道路.
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