Intent discovery is a crucial task in natural language processing, and it is increasingly relevant for various of industrial applications. Identifying novel, unseen intents from user inputs remains one of the biggest challenges in this field. Herein, we propose Zero-Shot-BERT-Adapters, a two-stage method for multilingual intent discovery relying on a Transformer architecture, fine-tuned with Adapters. We train the model for Natural Language Inference (NLI) and later perform unknown intent classification in a zero-shot setting for multiple languages. In our evaluation, we first analyze the quality of the model after adaptive fine-tuning on known classes. Secondly, we evaluate its performance in casting intent classification as an NLI task. Lastly, we test the zero-shot performance of the model on unseen classes, showing how Zero-Shot-BERT-Adapters can effectively perform intent discovery by generating semantically similar intents, if not equal, to the ground-truth ones. Our experiments show how Zero-Shot-BERT-Adapters outperforms various baselines in two zero-shot settings: known intent classification and unseen intent discovery. The proposed pipeline holds the potential for broad application in customer care. It enables automated dynamic triage using a lightweight model that can be easily deployed and scaled in various business scenarios, unlike large language models. Zero-Shot-BERT-Adapters represents an innovative multi-language approach for intent discovery, enabling the online generation of novel intents. A Python package implementing the pipeline and the new datasets we compiled are available at the following link: https://github.com/GT4SD/zero-shot-bert-adapters.

翻译：意图发现是自然语言处理中的关键任务，且在各类工业应用中日益重要。从用户输入中识别新颖、未见的意图仍是该领域最大的挑战之一。本文提出Zero-Shot-BERT-Adapters，一种基于Transformer架构的两阶段多语言意图发现方法，通过适配器进行微调。我们训练模型完成自然语言推理任务，随后在多语言零样本场景下执行未知意图分类。在评估中，我们首先分析基于已知类别进行自适应微调后的模型质量；其次评估其将意图分类转化为NLI任务的性能；最后验证模型在未见类别上的零样本表现，展示Zero-Shot-BERT-Adapters如何通过生成与真实意图语义高度相似（甚至相等）的意图来有效执行意图发现。实验表明，Zero-Shot-BERT-Adapters在已知意图分类与未知意图发现两种零样本场景中均优于多个基线模型。该流水线在客服领域具有广泛应用潜力，支持使用轻量级模型进行自动化动态分类，可在多种业务场景中便捷部署与扩展，有别于大型语言模型。Zero-Shot-BERT-Adapters代表了一种创新的多语言意图发现方法，能够在线生成新颖意图。实现该流水线的Python包及我们编译的新数据集可通过以下链接获取：https://github.com/GT4SD/zero-shot-bert-adapters。