Intent detection is a key component of modern goal-oriented dialog systems
that accomplish a user task by predicting the intent of users' text input.
There are three primary challenges in designing robust and accurate intent
detection models. First, typical intent detection models require a large amount
of labeled data to achieve high accuracy. Unfortunately, in practical scenarios
it is more common to find small, unbalanced, and noisy datasets. Secondly, even
with large training data, the intent detection models can see a different
distribution of test data when being deployed in the real world, leading to
poor accuracy. Finally, a practical intent detection model must be
computationally efficient in both training and single query inference so that
it can be used continuously and re-trained frequently. We benchmark intent
detection methods on a variety of datasets. Our results show that Watson
Assistant's intent detection model outperforms other commercial solutions and
is comparable to large pretrained language models while requiring only a
fraction of computational resources and training data. Watson Assistant
demonstrates a higher degree of robustness when the training and test
distributions differ.

研究比较不同意图检测方法，显示 Watson Assistant 具有高准确度，能较少使用计算资源和训练数据，并在训练和测试分布不同时表现出较高的鲁棒性。