Papers › AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and...
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Libo Qin, Xiao Xu, Wanxiang Che, Ting Liu
In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the single intent scenario, or simply incorporated an overall intent context vector for all tokens, ignoring the fine-grained multiple intents information integration for token-level slot prediction. In this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint multiple intent detection and slot filling, where we introduce an intent-slot graph interaction layer to model the strong correlation between the slot and intents. Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction. Experimental results on three multi-intent datasets show that our framework obtains substantial improvement and achieves the state-of-the-art performance. In addition, our framework achieves new state-of-the-art performance on two single-intent datasets.
In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.
Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Intent Detection | ATIS | AGIF | Accuracy | 97.1 | #13 of 16 | Archive leaderboard | report |
| Intent Detection | MixSNIPS | AGIF | Accuracy | 96.5 | #12 of 16 | Archive leaderboard | report |
| Intent Detection | MixSNIPS | AGIF | f1 macro | 98.6 | #12 of 16 | Archive leaderboard | report |
| Intent Detection | SNIPS | AGIF | Accuracy | 98.1 | #4 of 10 | Archive leaderboard | report |
| Slot Filling | ATIS | AGIF | F1 | 0.96 | #7 of 14 | Archive leaderboard | report |
| Slot Filling | MixSNIPS | AGIF | Micro F1 | 94.5 | #15 of 16 | Archive leaderboard | report |
| Slot Filling | SNIPS | AGIF | F1 | 94.8 | #4 of 10 | 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections