Intent Detection and Slot Filling

Intent Detection and Slot Filling is the task of interpreting user commands/queries by extracting the intent and the relevant slots.

Example (from ATIS):

Query: What flights are available from pittsburgh to baltimore on thursday morning
Intent: flight info
Slots: 
    - from_city: pittsburgh
    - to_city: baltimore
    - depart_date: thursday
    - depart_time: morning

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
CTRAN	98.46	98.07	CTRAN: CNN-Transformer-based network for natural language understanding	Official
SlotRefine + BERT	96.16	97.74	SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling	Official
SlotRefine	96.22	97.11	SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling	Official
Stack-Propagation + BERT	96.10	97.50	A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding	Official
JointBERT-CAE	96.1	97.50	CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task	Official
Co-interactive Transformer	95.90	97.70	A Co-Interactive Transformer for Joint Slot Filling and Intent Detection	Official
Heterogeneous Attention	95.58	97.76	Joint agricultural intent detection and slot filling based on enhanced heterogeneous attention mechanism
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

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
CTRAN	98.30	99.42	CTRAN: CNN-Transformer-based Network for Natural Language Understanding	Official
SlotRefine + BERT	97.05	99.04	SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling	Official
Stack-Propagation + BERT	97.00	99.00	A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding	Official
JointBERT-CAE	97.00	98.30	CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task	Official
Heterogeneous Attention	96.32	98.29	Joint agricultural intent detection and slot filling based on enhanced heterogeneous attention mechanism
Co-interactive Transformer	95.90	98.80	A Co-Interactive Transformer for Joint Slot Filling and Intent Detection	Official
Stack-Propagation	94.20	98.00	A Stack-Propagation Framework with Token-level Intent Detection for Spoken Language Understanding	Official
SlotRefine	93.72	97.44	SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling	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 Attention	88.80	97.00	Slot-Gated Modeling for Joint Slot Filling and Intent Prediction	Official