The recent advances in transfer learning techniques and pre-training of large
contextualized encoders foster innovation in real-life applications, including
dialog assistants. Practical needs of intent recognition require effective data
usage and the ability to constantly update supported intents, adopting new
ones, and abandoning outdated ones. In particular, the generalized zero-shot
paradigm, in which the model is trained on the seen intents and tested on both
seen and unseen intents, is taking on new importance. In this paper, we explore
the generalized zero-shot setup for intent recognition. Following best
practices for zero-shot text classification, we treat the task with a sentence
pair modeling approach. We outperform previous state-of-the-art f1-measure by
up to 16\% for unseen intents, using intent labels and user utterances and
without accessing external sources (such as knowledge bases). Further
enhancement includes lexicalization of intent labels, which improves
performance by up to 7\%. By using task transferring from other sentence pair
tasks, such as Natural Language Inference, we gain additional improvements.

探讨了用于意图识别的广义零射击模式，并使用句子对建模方法，通过任务转移进一步提高性能，使用语境编码器的预训练技术使模型适用于主要且适应不断变化的应用。