Dual Class Knowledge Propagation Network for Multi-label Few-shot Intent Detection (2023)

Abstract: Multi-label intent detection aims to assign multiple labels to utterances and attracts increasing attention as a practical task in task-oriented dialogue systems. As dialogue domains change rapidly and new intents emerge fast, the lack of annotated data motivates multi-label few-shot intent detection. However, previous studies are confused by the identical representation of the utterance with multiple labels and overlook the intrinsic intra-class and inter-class interactions. To address these two limitations, we propose a novel dual class knowledge propagation network in this paper. In order to learn well-separated representations for utterances with multiple intents, we first introduce a label-semantic augmentation module incorporating class name information. For better consideration of the inherent intra-class and inter-class relations, an instance-level and a class-level graph neural network are constructed, which not only propagate label information but also propagate feature structure. And we use a simple yet effective method to predict the intent count of each utterance. Extensive experimental results on two multi-label intent datasets have demonstrated that our proposed method outperforms strong baselines by a large margin.

## introduction multi-label intent detection aims to assign multiple labels to a single utterance rather than only one label since the user utterance often carries multiple different intents. as a practical task in real-world scenarios, multi-label intent detection attracts more attention with the rapid development of conversational ai @xcite . previous works have achieved promising performance in multi-label intent detection, but they rely on large amounts of labeled training samples (xu and @xcite @xcite . however, due to fast-emerging intents and rapidly changing domains, the lack of adequate labeled data for each intent is inevitable, thus motivating the multi-label few-shot intent detection @xcite . few-shot learning, a promising way to tackle data scarcity challenges, can recognize new classes from only a few labeled samples by exploiting the prior knowledge from source domain. recent works for multi-label intent detection mainly focus on threshold-based strategy which consists of relevance scoring and threshold estimation @xcite to improve performance. nevertheless, they suffer from the following two limitations. first, for multi-label utterances, most of them neglect the semantic connections between labels and input utterances and therefore learn the identical utterance representations for different labels, which confuses the similarity scoring. as figure 1 shows, the embeddings for request_date and request_time are the same, i.e., the embedding of utterance x 1 . furthermore, in few-shot scenarios, it's critical to obtain proper intra-class and inter-class relations from only a few samples, while previous works overlook the high-order interaction of samples and just usually use a specific metric to compute the relevance score directly, e.g., cosine similarity. to better capture more complete and more adaptive interactions among samples, we propose a novel dual class knowledge propagation network, containing an instance-level graph and a class-level graph. it not only propagates label information from labeled support samples to unlabeled queries but also propagates feature structure in their latent space. the instance-level graph smoothing the representations among highly related utterances reduces the intrinsic gap between augmented support samples and queries. the class-level graph provides label correlation and generates completed pseudo class-level representations for queries with the guidance of the instance-level graph. also, we design a calibration strategy to transfer label dependency into instance representations. then the two representations are fused to classify queries. finally, to select accurately multiple intents for an utterance, we employ an adaptive and powerful method to predict the number of intents. the contributions of this paper are as follows: (1) we propose a novel dual class knowledge propagation network (dckpn). dckpn incorporates label semantic connections to guide feature propagation and label information propagation. (2) we introduce a novel label-semantic augmentation method for multi-label samples to obtain more discriminative representations. and we employ a simple inference module to predict the number of intents in utterances adaptively. (3) to verify the effectiveness of our proposed model, we conduct a series of experiments on two datasets. the empirical study shows that our model can achieve state-of-the-art performance in comparison with other strong baselines. few-shot learning few-shot learning aims to recognize novel classes from a handful of annotated samples by leveraging the previous knowledge from source domains @xcite . recently, meta-learning has become a successful paradigm for solving the few-shot problem, which can be divided into three categories: model-based methods @xcite , optimizationbased methods @xcite and metric-based methods @xcite @xcite . however, these methods focus on single-label classification and only a few works investigate multi-label fewshot classification. previous studies focus on image domain @xcite , audio domain @xcite and sentiment analysis @xcite . ctlr @xcite is the first work to address multi-label few-shot intent detection tasks which proposes a meta-calibrated threshold mechanism and learns anchored label representation. however, it relies on a specific metric to estimate the relevance score, losing the inherent relations among samples and classes. ## conclusion in this paper, we propose a novel framework dckpn for multi-label few-shot intent detection to capture the semantic interactions. we first exploit label-semantic augmentation to extract discriminative representations for user utterances with multiple intents. then, we introduce an instance-level and a class-level propagation network to accomplish feature propagation and derive complete class representations. after that, we use the fused representations and an adaptive module to predict the possible multiple intents. extensive experiments verify the effectiveness of our method and identify the cause of performance improvements our method brings. for future work, we will explore joint learning integrated with slot filling in dialogue systems. wenjie zhang, junchi yan, xiangfeng wang, and hongyuan zha. 2018. deep extreme multi-label learning. in proceedings of the 2018 acm on international conference on multimedia retrieval, icmr, pages 100-107. dengyong zhou, olivier bousquet, thomas navin lal, jason weston, and bernhard schölkopf. 2003. learning with local and global consistency. in advances in neural information processing systems 16, pages 321-328. a label similarity (1 ). we can observe that (1) the classes in schedule are more similar and the classes in shopping are more distant. (2) the similarity of label description reflects co-occurrence between labels to some extent from the pragmatics view, e.g., in sc, the similarity of request date and request time is high, and in real world scenarios, human usually asks the two details together, as well as request date and request location, which is crucial in multi-label tasks with low resources.