Papers › A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding

A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding

22 Nov 2022arXiv:2211.12220archive 2025-07-28

Lizhi Cheng, Wenmian Yang, Weijia Jia

Multi-Intent Spoken Language Understanding (SLU), a novel and more complex scenario of SLU, is attracting increasing attention. Unlike traditional SLU, each intent in this scenario has its specific scope. Semantic information outside the scope even hinders the prediction, which tremendously increases the difficulty of intent detection. More seriously, guiding slot filling with these inaccurate intent labels suffers error propagation problems, resulting in unsatisfied overall performance. To solve these challenges, in this paper, we propose a novel Scope-Sensitive Result Attention Network (SSRAN) based on Transformer, which contains a Scope Recognizer (SR) and a Result Attention Network (RAN). Scope Recognizer assignments scope information to each token, reducing the distraction of out-of-scope tokens. Result Attention Network effectively utilizes the bidirectional interaction between results of slot filling and intent detection, mitigating the error propagation problem. Experiments on two public datasets indicate that our model significantly improves SLU performance (5.4\% and 2.1\% on Overall accuracy) over the state-of-the-art baseline.

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Tasks

Intent Detection Semantic Frame Parsing Slot Filling Spoken Language Understanding slot-filling

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Intent Detection	MixATIS	SSRAN	Accuracy	77.9	#11 of 15	Archive leaderboard	report
Intent Detection	MixSNIPS	SSRAN	Accuracy	98.4	#1 of 16	Archive leaderboard	report
Slot Filling	MixATIS	SSRAN	Micro F1	89.4	#4 of 15	Archive leaderboard	report
Slot Filling	MixSNIPS	SSRAN	Micro F1	95.8	#9 of 16	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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