Papers › A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
Yu Wang, Yilin Shen, Hongxia Jin
Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on these tasks . The most effective algorithms are based on the structures of sequence to sequence models (or "encoder-decoder" models), and generate the intents and semantic tags either using separate models or a joint model. Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. Most of these approaches use one (joint) NN based model (including encoder-decoder structure) to model two tasks, hence may not fully take advantage of the cross-impact between them. In this paper, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a decoder achieves state-of-the-art result on the benchmark ATIS data, with about 0.5% intent accuracy improvement and 0.9 % slot filling improvement.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Intent Detection | ATIS | Bi-model with decoder | Accuracy | 98.99 | #1 of 16 | Archive leaderboard | report |
| Slot Filling | ATIS | Bi-model with a decoder | F1 | 0.9689 | #2 of 14 | 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.
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