SPM : A Split-Parsing Method for Joint Multi-Intent Detection and Slot Filling (2023)

Abstract: In a task-oriented dialogue system, joint intent detection and slot filling for multi-intent utterances become meaningful since users tend to query more. The current state-of-the-art studies choose to process multi-intent utterances through a single joint model of sequence labelling and multi-label classification, which cannot generalize to utterances with more intents than training samples. Meanwhile, it lacks the ability to assign slots to each corresponding intent. To overcome these problems, we propose a Split-Parsing Method (SPM) for joint multiple intent detection and slot filling, which is a two-stage method. It first splits an input sentence into multiple sub-sentences which contain a single-intent, and then a joint single intent detection and slot filling model is applied to parse each sub-sentence recurrently. Finally, we integrate the parsed results. The sub-sentence split task is also treated as a sequence labelling problem with only one entity-label, which can effectively generalize to a sentence with more intents unseen in the training set. Experimental results on three multi-intent datasets show that our method obtains substantial improvements over different baselines.

## introduction with the development of natural language technologies, the task-oriented dialogue system has become a significant practical application. it is widely applied in many industrial scenarios. one critical component in the task-oriented dialogue system is spoken language understanding (slu) @xcite , which is further decomposed into two sub-tasks, namely intent detection and slot filling @xcite . the slot filling task aims to convert the user utterance into a bio label sequence of equivalent length. as for intent detection, it is essentially a sentence classification task which may have one or more labels. stateof-the-art studies tend to solve these two sub-tasks through a joint model @xcite , since slots and intents are highly correlated. previous literature in slu mainly focuses on utterances with a single intent. although classic models @xcite achieve remarkable performances on those single-intent datasets, they neglect the realistic situation where the user utterance may contain multiple intents. recently, researchers switch their attention to multi-intent benchmarks, such as mixatis and mixsnips @xcite @xcite ). an intuitive solution is to replace the original multi-class intent detection module into multi-label classification, see figure 1 @xcite . more advanced methods attempt to improve upon this backbone model. for example, @xcite proposes agif and gl-gin, which both integrate the correlation between slots and intents into model design. nonetheless, the separate prediction of multiple intents and slots leads to the failure of assigning appropriate slots to each intent. this mis-allocation of slots to intents may cause execution errors in a practical task-oriented dialogue system. furthermore, the generalization capability of previous models is less investigated regarding the number of intents. for instance, if the model is merely trained on samples containing 1-3 intents, it would perform poorly on utterances with more than 3 intents. to this end, we propose a split-parsing method (spm) for joint multi-intent detection and slot filling. spm is a two-stage slu system. at the first stage, the utterance is split into sub-sentences, each containing exactly one intent. these sub-sentences are independent and together constitute the complete semantic representation. at the second stage, each sub-sentence is parsed by a traditional slu model designed for single-intent. in this way, each slot is automatically aligned to their superior intent in the sub-sentence. eventually, all parsing results are aggregated through post-processing. as illustrated in figure 1 (b), the utterance "list la and what class is fare code q" is firstly split into two sub-sentences. next, each sub-sentence is fed into the joint model of intent detection and slot filling to obtain the corresponding intent and slots. the slotvalue pair "@xmath0. evidently, this method can effectively generalize to complicated utterances with more intents. the proposed spm is evaluated on two public english datasets (mixatis and mixsnips, @xcite , and a customized chinese dataset which is collected from an in-vehicle dialog system. experimental results demonstrate that the spm can 1) achieve nearly perfect performances on the subsentence split task at the first stage, 2) attain stable improvements compared to one-stage method regardless of the model choice at the second stage, and 3) generalize better towards examples containing more intents unseen during training. ## approach the differences between the one-stage slu system and our proposed two-stage slu system (spm) are illustrated in figure 1 . in the upper part, the user's multi-intent utterance is directly passed into a joint model of multi-label intent detection and slot filling, which is trained on multi-intent data. a token-level slots sequence and multiple intents are predicted, while it is not possible to assign each slot to the corresponding intent, since alignments between slots and intents are not modeled in this method. the below sub-figure of fig. 1 illustrates our proposed method, where a multi-intent sentence is first split into sub-sentences by our split model ( §3.1). these sub-sentences will be fed into a joint model of intent detection and slot filling separately. thus, we can catch slot results for each individual intent. meanwhile, the joint model of intent detection and slot filling exploited in the onestage slu can be applied into spm without any change, which is much portable and easy-to-use. ## experiments in this section, our spm is trained and tested in three datasets and compared with different baseline models. from our experimental results in english dataset, adding split models can improve slot performance and our models shows better generalization ability in more intents. the evaluation results in chinese dataset shows our spm is faster and generalize better compared with other two-stage slu system. ## future work due to split-parse approach, when there are errors in sentence segmentation, cumulative errors are inevitable. the next step is to optimize for the situation of cumulative errors. there are two directions for optimization. firstly, the accuracy of the sentence segmentation model can be further improved to reduce the probability of cumulative errors. secondly, in the slot filling model, the model can be designed to support utterances with 1-2 intents. when the sentence split model incorrectly splits multiple sub-sentences into one, a slot filling model that supports multiple intents can correctly tag slots and detect intents. ## conclusion in our paper, we propose a two-stage slu system based on the split-parsing method. with plugging our split model into the original slu system, the performance can be improved. compared with the commonly used one-stage slu systems, our method can better generalize in more intents unseen in training. meanwhile, the split-parsing method can effectively align slots with their corresponding intents in the segmented sentences. and compared with other two-stage slu systems using sequence-to-sequence as the split model, our model can achieve better performance of intent and slot filling detection with higher inference speed.