SPM: A Split-Parsing Method for Joint Multi-Intent Detection and Slot Filling
Sheng Jiang, Su Zhu, Ruisheng Cao, Qingliang Miao, Kai Yu · 2023
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 entitylabel, 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.