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require effective data usage and the ability to constantly update supported intents, adopting new ones, and abandoning outdated ones.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.

翻译：最近,在转让学习技巧和对大型背景化编码器进行预先培训方面的进步促进了实际应用的创新,包括对话助理。实际的意向确认需要有效的数据使用和不断更新支持的意图、采用新的意图和放弃过时的意图的能力。特别是,普遍零点范式正在取得新的重要性,在这种范式中,模型根据可见的意图进行训练,并根据可见和看不见的意图进行测试。在本文件中,我们探索了用于意向确认的普遍零点设置。在采用零点文本分类的最佳做法之后,我们用一种对称模式处理这项任务。我们利用意图标签和用户的言语,在不接触外部来源(例如知识基础)的情况下,以最多16英寸的速度对未知的意图实施以前的艺术F1计量。进一步的改进包括将意图标签法化,通过多达7 ⁇ 的方式提高性能。通过从自然语言推导等其他对句任务转移任务,我们取得了更多的改进。