A Coarse-to-Fine Prototype Learning Approach for Multi-Label Few-Shot Intent Detection (2024)

Abstract: Few-shot intent detection is a challenging task, particularly in scenarios involving multiple labels and diverse domains. This paper presents a novel prototype learning approach that combines the label synset augmentation and the coarse-to-fine prototype distillation for multi-label few-shot intent detection. To tackle the data scarcity issue and the lack of information for unseen domains, we propose to enhance the representations of utterances with label synset augmentation and refine the prototypes by distilling the coarse domain knowledge from a universal teacher model. To solve the multilingual intent detection in real-world dialogue systems, we fine-tune a cross-lingual teacher model to make our method fast adapt to different languages and re-annotate two non-English task-oriented dialogue datasets CrossWOZ and JMultiWOZ in multi-label form. Experimental results on one English and two non-English datasets demonstrate that our approach significantly outperforms existing methods in terms of accuracy and generalization across different domains.

## introduction intent detection which aims to identify intents behind user utterances is a core component of taskoriented dialogue systems @xcite , as its performance directly affects downstream decisions and policies. in real-world conversation scenarios, a single utterance could contain multiple intents, and the ways of expressing an intent are diverse. the data scarcity issue makes intent detection rather challenging as user intents constantly emerge in rapidly changing domains @xcite . therefore, the imperative to accurately recognize multiple intents in the low-data regime motivates the multi-label few-shot intent detection (fs-mlid). existing works mainly focus on the popular metric-based meta-learning paradigm for the fs-mlid task, which aims to learn a metric space that can make label predictions by calculating distances between query samples and prototypes of different classes. by training on a set of sampled fs-mlid tasks, the model learns general knowledge to rapidly generalize to new tasks with novel intent classes. in particular, ctlr @xcite proposes to estimate label-instance relevance scores and uses a meta-calibrated threshold to select multiple associated intent labels. dckpn @xcite constructs a dual class knowledge propagation network that combines label information and feature structure to guide intent prediction. however, existing methods neglect that it is difficult to estimate the class prototypes in low-resource settings, and they also lack domain information to predict novel classes @xcite , thereby diminishing the discriminability of the metric space and model generalization. in addition, previous works merely focus on monolingual setting and only conduct on english datasets @xcite . in contrast to english, most other languages lack sufficient annotated data to train high-quality intent detection models, which will ultimately hinder the application of task-oriented dialogue systems to a much wider spectrum of languages. to address the aforementioned issues, we revisit the fs-mlid task from a multilingual perspective and propose a novel coarse-to-fine prototype learning method (cfpl), which is shown in figure 1 . considering the scarcity of samples in few-shot learning and the rich semantic information beneath class labels, we propose to enhance the representations of utterances with label synset augmentation. specifically, we first generate a synset for each intent label using open multilingual wordnet @xcite , then we propose a refinement method to further eliminate the noise in the expanded label set. to precisely estimate the class prototypes in the absence of domain knowledge, we further devise the coarse-to-fine prototype distillation. in particular, intra-domain student prototypes are first learned through feature interactions at the instance and class levels for a specific dataset. then the inter-domain teacher prototypes that contain coarse domain knowledge from the teacher model are distilled into fine-grained prototypes for a specific dataset, which are further fused with intra-domain student prototypes to constitute more precise prototypes. moreover, we fine-tune a cross-lingual teacher model to make our method fast adapt to different languages. to verify this, we re-annotate two non-english task-oriented dialogue datasets in multi-label form, i.e., crosswoz @xcite and jmultiwoz @xcite , which contain multiple domains and thus can simulate the few-shot scenario in unseen domains. the contributions of this paper can be summarized as follows: ## conclusion in this paper, we propose a cfpl method for multilabel few-shot intent detection, which designs a label synset augmentation strategy to enhance the representations of the support data due to the data scarcity issue and proposes to refine the prototypes with knowledge distillation from a universal teacher model. to solve the multilingual intent detection, we fine-tune a cross-lingual teacher model to enable our method to adapt quickly to different languages. to verify our proposed method in detecting intents for multilingual dialogues, we reannotate two non-english task-oriented dialogue datasets crosswoz and jmultiwoz in multi-label form. experimental results demonstrate the superiority of our method.