Slot Models
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Intent Detection and Slot Filling is the task of interpreting user commands/queries by extracting the intent and the relevant slots.
Example (from ATIS):
ATIS
ATIS (Air Travel Information System) (Hemphill et al.) is a dataset by Microsoft CNTK. Available from the github page. The slots are labeled in the BIO (Inside Outside Beginning) format (similar to NER). This dataset contains only air travel related commands. Most of the ATIS results are based on the work here.
| Model | Slot F1 Score | Intent Accuracy | Paper / Source | Code |
|---|---|---|---|---|
| Bi-model with decoder | 96.89 | 98.99 | A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling | |
| Stack-Propagation + BERT | 96.10 | 97.50 | A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding | Official |
| Stack-Propagation | 95.90 | 96.90 | A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding | Official |
| Attention Encoder-Decoder NN | 95.87 | 98.43 | Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling | |
| SF-ID (BLSTM) network | 95.80 | 97.76 | A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling | Official |
| Context Encoder | 95.80 | NA | Improving Slot Filling by Utilizing Contextual Information | |
| Capsule-NLU | 95.20 | 95.00 | Joint Slot Filling and Intent Detection via Capsule Neural Networks | Official |
| Joint GRU model(W) | 95.49 | 98.10 | A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding | |
| Slot-Gated BLSTM with Attension | 95.20 | 94.10 | Slot-Gated Modeling for Joint Slot Filling and Intent Prediction | Official |
| Joint model with recurrent slot label context | 94.64 | 98.40 | Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks | Official |
| Recursive NN | 93.96 | 95.40 | JOINT SEMANTIC UTTERANCE CLASSIFICATION AND SLOT FILLING WITH RECURSIVE NEURAL NETWORKS | |
| Encoder-labeler Deep LSTM | 95.66 | NA | Leveraging Sentence-level Information with Encoder LSTM for Natural Language Understanding | |
| RNN with Label Sampling | 94.89 | NA | Recurrent Neural Network Structured Output Prediction for Spoken Language Understanding | |
| Hybrid RNN | 95.06 | NA | Using recurrent neural networks for slot filling in spoken language understanding. | |
| RNN-EM | 95.25 | NA | Recurrent neural networks with external memory for language understanding | |
| CNN-CRF | 94.35 | NA | Convolutional neural network based triangular crf for joint intent detection and slot filling |
SNIPS
Slot Machine Models
SNIPS is a dataset by Snips.ai for Intent Detection and Slot Filling benchmarking. Available from the github page. This dataset contains several day to day user command categories (e.g. play a song, book a restaurant).
| Model | Slot F1 Score | Intent Accuracy | Paper / Source | Code |
|---|---|---|---|---|
| Stack-Propagation + BERT | 97.00 | 99.00 | A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding | Official |
| Stack-Propagation | 94.20 | 98.00 | A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding | Official |
| Context Encoder | 93.60 | NA | Improving Slot Filling by Utilizing Contextual Information | |
| SF-ID (BLSTM) network | 92.23 | 97.43 | A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling | Official |
| Capsule-NLU | 91.80 | 97.70 | Joint Slot Filling and Intent Detection via Capsule Neural Networks | Official |
| Slot-Gated BLSTM with Attension | 88.80 | 97.00 | Slot-Gated Modeling for Joint Slot Filling and Intent Prediction | Official |
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