Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs (2022)

Abstract: Recent graph-based models for joint multiple intent detection and slot filling have obtained promising results through modeling the guidance from the prediction of intents to the decoding of slot filling.However, existing methods (1) only model the unidirectional guidance from intent to slot; (2) adopt homogeneous graphs to model the interactions between the slot semantics nodes and intent label nodes, which limit the performance.In this paper, we propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the mutual guidances between the two tasks.In the first stage, the initial estimated labels of both tasks are produced, and then they are leveraged in the second stage to model the mutual guidances.Specifically, we propose two heterogeneous graph attention networks working on the proposed two heterogeneous semantics-label graphs , which effectively represent the relations among the semantics nodes and label nodes.Experiment results show that our model outperforms existing models by a large margin, obtaining a relative improvement of 19.3% over the previous best model on MixATIS dataset in overall accuracy.

## introduction spoken language understanding (slu) @xcite is a fundamental task in dialog systems. its objective is to capture the comprehensive semantics of user utterances, and it typically includes two subtasks: intent detection and slot filling @xcite . intent detection aims to predict the intention of the user utterance and slot filling aims to extract additional information or constraints expressed in the utterance. recently, researchers discovered that these two tasks are closely tied, and a bunch of models @xcite @xcite @xcite are proposed to combine the single-intent detection and slot filling in multi-task frameworks to leverage their correlations. however, in real-world scenarios, a user usually expresses multiple intents in a single utterance. to this end, @xcite begin to tackle the multi-intent detection task and (gangadharaiah and narayanaswamy, 2019) make the first attempt to jointly model the multiple intent detection and slot filling in a multi-task framework. @xcite propose an agif model to adaptively integrate the fine-grained multi-intent prediction information into the autoregressive decoding process of slot filling via graph attention network (gat) @xcite . and @xcite further propose a non-autoregressive gat-based model which enhances the interactions between the predicted multiple intents and the slot hidden states, obtaining state-of-the-art results and significant speedup. despite the promising progress that existing multi-intent slu joint models have achieved, we discover that they suffer from two main issues: in this paper, we propose a novel model termed co-guiding net to tackle the above two issues. for the first issue, co-guiding net implements a two-stage framework as shown in fig. 1 (b ). the first stage produces the initial estimated labels for the two tasks and the second stage leverages the estimated labels as prior label information to allow the two tasks mutually guide each other. for the second issue, we propose two heterogeneous semantics-label graphs (hslgs): (1) a slot-to-intent semantics-label graph (s2i-slg) that effectively represents the relations among the intent semantics nodes and slot label nodes; (2) an intent-to-slot semantics-label graph (i2s-slg) that effectively represents the relations among the slot semantics nodes and intent label nodes. moreover, two heterogeneous graph attention networks (hgats) are proposed to work on the two proposed graphs for modeling the guidances from slot to intent and intent to slot, respectively. experiment results show that our co-guiding net significantly outperforms previous models, and model analysis further verifies the advantages of our model. the contributions of our work are three-fold: (1) we propose co-guiding net foot_0 , which implements a two-stage framework allowing multiple intent detection and slot filling mutually guide each other. we make the first attempt to achieve the mutual guidances between the two tasks. next, before diving into the details of coguiding net's architecture, we first introduce the construction of the two heterogeneous graphs. ## related work the correlations between intent detection and slot filling have been widely recognized. to leverage them, a group of models @xcite @xcite @xcite @xcite @xcite @xcite are proposed to tackle the joint task of intent detection and slot filling in a multi-task manner. however, the intent detection modules in the above models can only handle the utterances expressing a single intent, which may not be practical in real-world scenarios, where there are usually multi-intent utterances. to this end, @xcite propose a multi-intent slu model, and @xcite propose the first model to jointly model the tasks of multiple intent detection and slot filling via a slot-gate mechanism. furthermore, as graph neural networks have been widely utilized in various tasks @xcite @xcite , they have been leveraged to model the correlations between intent and slot. @xcite propose an adaptive graph-interactive framework to introduce the fine-grained multiple intent information into slot filling achieved by gats. more recently, @xcite propose another gat-based model, which includes a nonautoregressive slot decoder conducting parallel decoding for slot filling and achieves the state-of-theart performance. our work also tackles the joint task of multiple intent detection and slot filling. existing methods only model the one-way guidance from multiple intent detection to slot filling. besides, they adopt homogeneous graphs and vanilla gats to achieve the interactions between the predicted intents and slot semantics. in contrast, we (1) achieve the mutual guidances between the two tasks; (2) propose the heterogeneous semantics-label graphs to represent the dependencies among the semantics and predicted labels; (3) we propose the heterogeneous graph attention network to model the semanticslabel interactions on the heterogeneous semanticslabel graphs. ## conclusion in this paper, we propose a novel co-guiding net based on a two-stage framework that allows the two tasks to guide each other in the second stage using the predicted labels at the first stage. to represent the relations among the semantics node and label nodes, we propose two heterogeneous semanticslabel graphs, and two heterogeneous graph attention networks are proposed to model the mutual guidances between intents and slots. experiment results on benchmark datasets show that our model significantly outperforms previous models. future work will focus on leveraging syntactic information to enhance utterance understanding.