In this work, we investigate several machine learning methods to tackle the problem of intent classification for dialogue utterances. We start with bag-of-words in combination with Naïve Bayes. After that, we employ continuous bag-of-words coupled with support vector machines (SVM). Then, we follow long short-term memory (LSTM) networks, which are made bidirectional. The best performing model is hierarchical, such that it can take advantage of the natural taxonomy within classes. The main experiments are a comparison between these methods on an open sourced academic dataset. In the first experiment, we consider the full dataset. We also consider the given subsets of data separately, in order to compare our results with state-of-the-art vendor solutions. In general we find that the SVM models outperform the LSTM models. The former models achieve the highest macro-F1 for the full dataset, and in most of the individual datasets. We also found out that the incorporation of the hierarchical structure in the intents improves the performance.

中文翻译：

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对话话语的意图分类

在这项工作中，我们研究了几种机器学习方法来解决对话话语的意图分类问题。我们从词袋结合朴素贝叶斯开始。之后，我们采用连续词袋与支持向量机（SVM）相结合。然后，我们遵循双向的长短期记忆 (LSTM) 网络。性能最好的模型是分层的，这样它就可以利用类内的自然分类法。主要实验是在开源学术数据集上对这些方法进行比较。在第一个实验中，我们考虑完整数据集。我们还分别考虑给定的数据子集，以便将我们的结果与最先进的供应商解决方案进行比较。总的来说，我们发现 SVM 模型优于 LSTM 模型。前一个模型在整个数据集和大多数单个数据集中实现了最高的宏 F1。我们还发现在意图中加入层次结构可以提高性能。