Intent Detection
88 papers with code • 15 benchmarks • 19 datasets
Intent Detection is a vital component of any task-oriented conversational system. In order to understand the user’s current goal, the system must leverage its intent detector to classify the user’s utterance (provided in varied natural language) into one of several predefined classes, that is, intents. However, the performance of intent detection has been hindered by the data scarcity issue, as it is non-trivial to collect sufficient examples for new intents. How to effectively identify user intents in few-shot learning has become popular.
Source: Few-shot Intent Detection Datasets, Baselines and Results
Source: Efficient Intent Detection with Dual Sentence Encoders
Benchmarks
These leaderboards are used to track progress in Intent Detection
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Libraries
Use these libraries to find Intent Detection models and implementations
ATIS
SNIPS
BANKING77
CLINC150
Most implemented papers
BERT for Joint Intent Classification and Slot Filling
Intent classification and slot filling are two essential tasks for natural language understanding.
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition.
Zero-shot User Intent Detection via Capsule Neural Networks
congyingxia/ZeroShotCapsule
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EMNLP 2018
User intent detection plays a critical role in question-answering and dialog systems.
Efficient Intent Detection with Dual Sentence Encoders
Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i. e., in few-shot setups).
Joint Slot Filling and Intent Detection via Capsule Neural Networks
czhang99/Capsule-NLU
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ACL 2019
Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
google/uncertainty-baselines
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Mahalanobis distance (MD) is a simple and popular post-processing method for detecting out-of-distribution (OOD) inputs in neural networks.
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
MiuLab/SlotGated-SLU
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NAACL 2018
Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights.
A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
ZephyrChenzf/SF-ID-Network-For-NLU
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ACL 2019
The joint model for the two tasks is becoming a tendency in SLU.
DELTA: A DEep learning based Language Technology plAtform
didi/delta
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In this paper we present DELTA, a deep learning based language technology platform.
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
LeePleased/StackPropagation-SLU
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IJCNLP 2019
In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge.
