Intent Detection
117 papers with code • 19 benchmarks • 24 datasets
Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations. A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context. One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in or unrecognized user.
Source: Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers
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 implementationsDatasets
MultiWOZ
ATIS
SNIPS
BANKING77
CLINC150
Dialogue State Tracking Challenge
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.
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).
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.
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.
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.
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.
